We're well into the AI hype cycle. Enterprises across industries are scrambling to understand how they can leverage artificial intelligence to augment their businesses. 


Unfortunately, enterprises are failing [80-90%](https://fortune.com/2022/07/26/a-i-success-business-sense-aible-sengupta/) of the time as they try to implement AI into their business. Oftentimes this means they don’t reach ROI or their AI projects simply can’t get to launch due to internal friction.


In general, innovative enterprises will take one of two approaches to AI deployment within their organizations. They either A) train an AI model from scratch or fine-tune an open-source model in-house or B) deploy third-party tools, often point solutions, within existing business processes. 


Too often these approaches begin from a mindset that "AI" is the panacea for complex operations problems. In this blog post, we will focus on the challenges enterprises face when taking these approaches to leverage AI.


Let’s start with Challenge #1. 

## Challenge #1: In-House AI Training and Fine-Tuning Is Too Costly and Complex
The organizational requirements for training an enterprise-scale AI model from scratch or fine-tuning an existing model for a specific use case are extremely high. The main barriers to building a successful model or effectively fine-tuning one are cost and complexity. 


### Costs of Training An In-House Model
Costs for building a large-scale model like a large language model (LLM) [reportedly](https://www.linkedin.com/pulse/selling-data-44-cost-creating-ai-ayal-steinberg/) start around $2-4 million, with millions in recurring costs to run them. These costs include expert AI talent, human data trainers, infrastructure, and compute. 


Enterprise leaders can be lured into spearheading in-house AI model training projects that either don’t yield an effective model or creep far past their forecasted costs. That’s because of an outdated approach to model development. 


In the beginning, AI researchers trained large language models (LLMs) using data easily collected on the internet. However, just because data is easily available does not mean it is honest, helpful, or harmless to the user. 


On the contrary, data easily collected from the internet can be harmful, biased, and toxic, the consequences of which can be worse for the enterprise than an ineffective model. Like a child, these nascent models require supervision and reinforcement of good behavior in order to align with user preferences. 


Supervised fine tuning (SFT) and reinforcement learning (RL) are the methods used by researchers to remove bias and harmful answers from responses generated by LLMs. But performing these techniques to align an AI model is too costly for enterprises to perform in-house. 


Thus, enterprise leaders can face project failure or perpetually chase ROI by exclusively managing AI model training internally. 

### Costs for Fine-Tuning an Existing Model
Fine-tuning an existing model is much cheaper. But the investment is still larger than enterprises might anticipate. 


One of the most burdensome costs enterprises will incur in fine-tuning is data preparation. That’s because out-of-the-box AI tools don’t fully support complex business processes. 


To effectively support a specific use case, open-source models will need to be fine-tuned on cleanly structured human-in-the-loop data. Enterprises will often chase fast AI solutions by feeding AI models with poorly structured, incomplete datasets that produce unuseful outputs. 


A more effective data preparation approach in the fine-tuning lifecycle first involves a strong data preprocessing phase. Data preprocessing is a largely manual process that turns unstructured data into a dataset ready for an AI model to be trained on. 


Then, the model will need to be fine-tuned on specifically created datasets, like demonstration data, to align the model with the needs of the specific use case. An example of demonstration data is a dataset of thousands of meticulously crafted conversation examples between a person and a chatbot for a customer support use case. 


These steps become too costly for internal teams because they can’t be automated. People-driven tasks don’t scale well. 

### How Invisible Solves the Cost And Complexity Problem
Scaling AI training and fine-tuning is complex and labor-intensive. Most enterprises aren’t equipped to do it without outside help. 


Many enterprise leaders will be familiar with and turn to BPOs. However, traditional BPOs can’t provide the quality of human-in-the-loop data required to produce or fine-tune an effective model. 


Invisible provides a complete [AI training solution](https://www.invisible.co/ai-training/) for enterprises that are building foundational models or fine-tuning open-source models for specific use cases. Our platform is 100% configurable to enable cost-effective, human-in-the-loop AI training and fine-tuning at scale. 


Our global team of AI trainers receives steps unique to their expertise through our platform. Each step is performed via a simple user interface specifically designed for that step’s goal. 


See that capability in action with an example of a Reinforcement Learning from Human Feedback (RLHF) step on our platform: 

![blog-test.gif](https://honest-deft-cloth.media.strapiapp.com/blog_test_71057bfcef.gif)


In this example, an AI trainer provides a prompt and then receives a sample of four outputs to rank for accuracy and helpfulness. They can also report if an output is toxic or harmful, providing metadata for why that’s the case. 


Without a solution like Invisible, enterprises are forced to build this infrastructure themselves and hire expensive talent, or turn to a BPO that provides poor-quality data. 


Ready to harness AI’s potential? [Speak to the team](https://www.invisible.co/get-started/).

Fine-tuning Foundational AI Models is Too Costly and Complex for Most Enterprises

In the first blogpost of this series on data enrichment, we highlighted the challenges companies face in harnessing the power of their data due to issues like raw and incomplete datasets. Now, let’s dive deeper into the topic, focusing on practical examples of bad versus good data quality.

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## Practical B2B Data Enrichment Use Cases
Firstly, let’s examine an example sales lead dataset, extracted from a CRM that is poorly structured:

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![Bad Quality 1.png](https://honest-deft-cloth.media.strapiapp.com/Bad_Quality_1_fdbcf9e0da.png)


This table suffers from several issues including missing datafields and variation in structuring, making it unusable. 


A data enrichment process would normalize data structuring and use third-party sources to fill in the gaps and remove errors to produce a much more complete sheet as seen below. ‍In this example, the resulting table post enrichment process is much more useful. The growth team has all of the relevant data they need to reach out to their leads.


![Good Quality 1.png](https://honest-deft-cloth.media.strapiapp.com/Good_Quality_1_49ce99558c.png)
‍

Let's explore another example:: 

‍
**Product Feed Management**: A product feed (aka: product catalog) organizes crucial product data including category, description, price, and dimensions into a searchable database for customers of E-commerce marketplaces.. 

‍
The largest eCommerce marketplaces receive product data from tens of thousands of vendors, all in different formats, schemas, and structures. Miscategorization of products or inaccurate descriptions can result in a database of products like the example below. Unfortunately, these inaccuracies cause products to never be found by consumers, becoming dreaded deadstock, or non-performing inventory.


![Bad Quality 2.png](https://honest-deft-cloth.media.strapiapp.com/Bad_Quality_2_523171a453.png)


An enriched product feed looks more like the example below, with a complete set of correctly categorized attributes that boost search engine visibility improving customer experience, and sales as a result. 


![Good Quality 2.png](https://honest-deft-cloth.media.strapiapp.com/Good_Quality_2_7505f8367f.png)


How do you enrich a database of hundreds of thousands of products, and do so flexibly and at scale? By creating and executing a process using AI, automation and an educated global workforce. See how we did it for one of the two largest retailers in the world. 

‍
**KYC & Regulatory Compliance**: FinTech & Financial Services leaders leverage data enrichment to satisfy regulatory requirements, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations. Often, customer information is sourced from several databases including government forms, watchlists, and public records, each with their own unique schema and format.


In this example, an incomplete dataset could look like this:


![Bad Quality 3.png](https://honest-deft-cloth.media.strapiapp.com/Bad_Quality_3_37432e54b8.png)


As you can see in the table above, this dataset is sub-optimal and would cost fintech teams several hours of manual review and enrichment to become valuable. This delay causes friction for the customer onboarding process, which could lead to churn.


A complete dataset is required in order to meet regulatory requirements, and thus enriching the unstructured data is a crucial task for Fintech teams. Using automation and an intelligent workforce to QA the process, thousands of rows of unstructured data can become enriched without requiring full-time-employee’s valuable time, and as a result, satisfy government requirements. An example of this type of dataset is below.
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![Good Quality 3.png](https://honest-deft-cloth.media.strapiapp.com/Good_Quality_3_3684322acc.png)

### Bottom Line
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In the above examples, we have outlined what poor data quality can look like and the operational problems businesses face if the data is left untransformed. We have also explored what a good dataset looks like and the benefits businesses experience from performing data enrichment.


In the final post of this three-part series, we will discuss how technology nor people alone are the optimal solution for data enrichment. A unique combination of the two are required and augmented through a process engine to deliver enriched databases at scale. 


Ready to see what good data quality does for your team? [Reach out](https://www.invisible.co/get-started/).

The Difference Between Bad and Good Data Quality & Why It Matters

With budgets and teams for AI innovation materializing at light speed, enterprises should know what it takes to train a model to solve enterprise-scale problems and yield a return on investment. In particular, an enterprise should care about HHH AI. 


HHH is a framework popularized by [Anthropic](https://www.anthropic.com/index/training-a-helpful-and-harmless-assistant-with-reinforcement-learning-from-human-feedback) for how helpful, honest, and harmless a model is. A model that meets these standards must be aligned with human preference through human-generated data in order to meet the needs of users and enterprises.


In this blog post, we will explore what makes a model helpful, honest, and harmless, particularly focusing on large language models (LLMs). Then, we will explain how Invisible helps the leading firms and enterprises to align their models. 


Let’s dive in. 

## Why is HHH Important? 


Building AI isn’t about just creating smart machines. Beyond solving complex enterprise business problems, HHH is a framework that ensures AI is beneficial for humanity. 

The alternative is a technology that risks harming the people who use it. Unfortunately, models are not HHH right out of the box. 

### The Pitfalls of a Vanilla Model
Because building a LLM from scratch is [extremely expensive](https://hai.stanford.edu/news/new-approach-trains-large-language-models-half-time#:~:text=But%20a%20handful%20of%20large,or%20hundreds%20of%20times%20that.), let’s begin where most enterprises do: exploring vanilla models. A vanilla large language model (LLM) is a basic, general-purpose model without tweaks or special enhancements that generates text based on user prompts. 


Vanilla LLMs are trained on a massive corpus of internet text providing them with a broad scope, but this breadth can sometimes come at the cost of depth in specific topics. As a result, these types of models will be able to engage in a topic area that’s a mile wide but an inch deep. 


Vanilla models that have not been fine-tuned are prone to “hallucinating” answers, or generating content that seems plausible but is [factually incorrect](https://www.washingtonpost.com/technology/interactive/2023/ai-chatbot-learning/) or can cause harm.  For an enterprise business, that means deploying a vanilla model leads to two problems: 

- **Unhelpful Outputs**: Enterprises may create, for example, a conversational AI application like a chatbot that’s supported by a vanilla model that is capable of answering a diverse set of questions, but will lack the domain specialization needed to be truly helpful to a user.
- **Harmful Outputs**: Text datasets sourced from the internet are likely to include biased and even harmful content, and models trained exclusively on this data can pass this content onto internal users or customers. 


To avoid hallucinations and solve enterprise-scale problems through their AI investment, enterprises need to adopt a helpful, honest, and harmless AI framework centered around fine-tuning. 


### Defining What HHH Really Means
Let’s go deeper into what HHH means, with the help of [researchers at Anthropic](https://www.arxiv-vanity.com/papers/2112.00861/). 

#### What is Helpful AI? 
AI systems that are helpful:
- Are trained and fine-tuned with users’ needs and values in mind
- Clearly attempt to conduct the operation prompted by the user, or suggest an alternative approach when the task requires it 
- Enhance productivity, save time, or make tasks easier for users within a given use case or range of use cases
- Are accessible to users across a broad spectrum of abilities and expertise

#### What is Honest AI? 
AI systems that are honest: 
- Provide accurate information when they can, and communicate clearly to users when they can’t produce an accurate output  
- Express uncertainty and the reason behind it
- Are developed and operate transparently so users can understand how they work and trust what they generate

#### What is Harmless AI? 
AI systems that are harmless: 
- Don’t comply when prompted to perform a dangerous task
- Are trained within frameworks that transparently and actively mitigate bias
- Don’t discriminate or demonstrate bias explicitly or implicitly
- Communicate sensitively when engaging with a user on a sensitive topic

## The Key to HHH AI
The best way to reinforce a model’s helpfulness, honesty, and harmlessness is through human feedback. Reinforcement learning from human feedback (RLHF) and supervised fine-tuning (SFT) are two increasingly popular techniques for aligning models with HHH in mind. 


- **RLHF**: Human AI trainers rank, rate, and edit a series of model outputs to reinforce the generation of responses that align with human preferences. 
- **SFT**: Human AI trainers, often domain experts, produce demonstration data like example conversations between a chatbot and a user to train a model to align with a specific task. 

In general, enterprises can’t conduct these tasks on their own because they’re labor-intensive and complex. Thousands of hours of RLHF and SFT tasks need to be performed, at times with experts in specific industries or fields, to align a model to human preferences and use cases. 


### How Invisible Trains HHH AI 
Invisible, the operations innovation company, promotes HHH with an innovative, end-to-end approach to fine-tuning models in three distinct ways: 

1. **Ethically creating human-generated data at scale**: With our platform, we do what no point solution or human-only solution can do on their own: optimally align AI. Our platform assigns AI trainers tasks that they are specialized in, presents a simple interface configured to those specific tasks, and collects data structured perfectly for a model to ingest. See an example of an RLHF task on our platform: 
![blog-test.gif](https://honest-deft-cloth.media.strapiapp.com/blog_test_71057bfcef.gif)

2. **Putting the best people behind the model**: An innovative and agile recruitment engine enables us to find and hire AI trainers that are representative of the population at large and have expertise across domains. Through this approach, we are able to limit demographic concentration among teams training models and provide the unique level of domain expertise required to fine-tune models for enterprise use cases. For one leader in AI, we hired 250+ AI trainers in three months, including domain experts with advanced degrees. 

3. **Red Teaming**: We root out inaccuracy, bias, and toxicity in a model through a technique called Red Teaming. Our expert team deliberately attempts to elicit a harmful and untrue output from a model, and then flags vulnerabilities in the model. 

Ready to achieve a return on your AI investment? [Let’s chat](https://www.invisible.co/get-started/). 

How to Train Helpful, Honest, and Harmless AI

Welcome back to the Invisible data enrichment mini-series. We’ve explored why data enrichment is critical, and discussed examples of good and bad data quality. In this blog, we explore what valuable characteristics to consider when approaching high-volume data enrichment.


Leading enterprises and scaling startups’ fortunes are made and lost on their ability to leverage their data. Unfortunately, data quality can fall down the list of priorities, and when it is assigned to an internal team, they are often overwhelmed and under-resourced. Some turn to BPO’s to offload their burden, but are underwhelmed with middling results that don’t solve their core problem.  


Let’s explore why. 

‍

### What Stops A Team of People or Tech Solution From Enriching Data?
Data enrichment is complicated by nuance. Technology struggles to reconcile the structuring of data across formatting issues, and can’t employ the human intuition needed to thoughtfully research new data from third-party sources. 

Internal teams face another series of challenges. They’re well-suited to perform the task at low volumes, but are unable to invest time and resources to prioritize data enrichment among other tasks as volumes scale.

BPOs tackle this problem by matching volume of data with volume of people, but efficiency and quality in the process falter because people-only processes do not scale well. 

‍

### The Solution: People, AI, and Platform

Invisible is uniquely positioned to provide scaled data enrichment across different use cases. We leverage strategic expertise and a prorietary process engine to break down problems into digestible steps, identify the steps that are best performed by automation and AI, and steps that require the intuition of our educated, global workforce. 


All work is excuted on our workforce management platform. Its extreme flexibility allows us to configure individual steps that are tailor made for AI-enabled data enrichment. 


Here’s what that looks like for one of our clients, in practice:  



![AI-enabled data enrichment.png](https://honest-deft-cloth.media.strapiapp.com/AI_enabled_data_enrichment_fbba80c4f4.png)
One of the world's largest retailers, attracting over 50 million online visitors per month, was facing a major operational hurdle: their SKU/product data was incomplete. Crucial details such as category, color, material, and size were absent, rendering products unsearchable on their website. 

This led to a significant, strategically critical challenge, as the retailer found itself lacking the necessary processes, personnel, and technology to effectively address the problem.

Within 16 days, we implemented a unique solution to enrich thousands of products across seven unique fields. The generative AI model GPT-4 takes a first pass at enriching data fields from scraped data, with an additional layer of AI flagging what is likely to contain errors. 

Operators review those flags, manually enrich what’s left, and deliver a fully enriched product listing. Through this seamless integration of human expertise and cutting-edge technology, we achieved exceptional time-to-value for the client.

In 16 days, we enriched 50,000 SKUs. 3,137 items went from being unsold to generating approximately $900k in revenue for the first time, and 49% of products saw an increase in conversions overall. 

Ready to explore how data enrichment can augment your business? [Contact us](https://www.invisible.co/get-started/).

Automation & An Educated Workforce Delivers Optimal Data Enrichment at Scale

In an increasingly digital business environment, companies are generating and have access to a higher volume of data than ever before. But most aren’t equipped to fully leverage it, leading to raw and incomplete datasets being found in every function of every company.


While they’re collecting digital dust, they could be boosting operational efficiency and improving strategic decision-making. This underutilized data should be considered an important asset, one that can boost operational efficiency and empower key stakeholders to make more informed decisions.


The key to turning underutilized raw data into a strategic asset is data enrichment. 


In this mini-series, we’ll discuss three dimensions of data enrichment: 

- Why does your data need to be enriched? What are the best ways to enrich data?
- What is the difference between good and bad data quality? Why is this important to your business?
- Which types of companies are most susceptible to poor data quality and would benefit from data enrichment? 
‍

Let’s dive into the first dimension. We will cover dimensions 2 and 3 in later posts.

‍
### Why Is Data Enrichment Necessary? 
Leading enterprises procure data through a process of data extraction from several internal and external sources, as shown below: 



![Data Extraction.png](https://honest-deft-cloth.media.strapiapp.com/Data_Extraction_6791329a2c.png)


Each original data source may have a completely different schema than the others, resulting in errors and omissions in the combined database. Restructuring this data to be usable can take hundreds of FTE hours; time that should be spent on higher functioning tasks. 

#### What is Data Enrichment?
Data enrichment transforms the resulting dataset into a strategic asset. It involves taking "first-party" data collected directly from a source like audiences or customers and supplementing it with "third-party" information from external sources. 


The goal of the data enrichment process is to improve data quality and value, which yields stronger decision-making. Here are the key elements of data enrichment:
‍

**Cleaning**: Removing or correcting erroneous data. This can include identifying and eliminating duplicate entries, correcting misspellings, and verifying data accuracy.
‍

**Augmentation**: Adding third-party data or additional data sources to existing data. One example: enriching customer data by adding demographic or behavioral information from other databases.


**Integration**: Combining data from different sources to provide a more comprehensive view. This could mean merging datasets or linking related data points across datasets.


**Transformation**: Converting data from its original form into a form more suitable for analysis. Transformation can be as simple as QOL improvements like the ease of filtering, or calculations, aggregations, or other methods to derive new data points.


**Validation**: Verifying that data is accurate, relevant, and useful. Usually, this includes checking data against known values or validating it against set rules or requirements.


At Invisible, we’re uniquely positioned to provide scaled data enrichment across a multiplicity of use cases. Through a unique combination of AI and a skilled workforce, both enabled by our process platform, data operations are our bread & butter. 


We break down data enrichment tasks into their smallest steps. We then leverage strategic expertise to identify steps that can be performed by automation tech or AI, as well as where to apply our skilled operators through our process engine.


For high volume data-enrichment processes, we utilize our extremely agile recruiting engine that scales to meet demand. As we define a process, we add automation and AI where possible to increase efficiency, improving quality while simultaneously decreasing unit cost. 

In this post, we have described what data enrichment is, and why it’s a requirement for leading enterprises. Read part 2 and part 3 of this mini-series to learn more about good vs. bad data quality, as well the types of companies who are positioned to benefit the most from data enrichment.


Bogged down by incomplete and messy datasets? [Get in touch](https://www.invisible.co/get-started/).

Data Enrichment: How to Turn Data into a Strategic Asset

The healthcare industry is riddled with administrative inefficiencies that diminish patient care quality, inflate costs, and lead to a colossal waste of human resources. At the heart of the problem lie redundant processes that primarily sit in the front and back office.   


Back office processes including billing procedures and documentation processing are financial drags on enterprise healthcare systems because they are time-consuming, labor-intensive, and prone to error. Meanwhile, patient inquiries can surge and significantly slow down operators in the front office. 


Like enterprises in other industries, many healthcare organizations embrace AI as the silver bullet that will address these inefficiencies. AI point solutions can streamline some processes, but relying on them exclusively could potentially exacerbate inefficiencies. 


In this blog post, we will dive into why healthcare leaders are turning to AI, the risks of flawed approaches organizations may take in deploying it, and a practical framework for effectively applying AI in enterprise.


## AI Adoption Accelerating in Healthcare
As recently as March 2022, [experts questioned](https://www.brookings.edu/articles/why-is-ai-adoption-in-health-care-lagging/) whether healthcare was taking too long to adopt AI compared to other industries. AI adoption in healthcare has accelerated rapidly since, matching the increasing availability of the technology in the market.


Now, more than half of healthcare executives [report](https://healthitanalytics.com/news/almost-half-of-health-systems-use-ai-for-workforce-issues) deploying AI to tackle workforce challenges according to a recent survey. The survey [found](https://www.businesswire.com/news/home/20230314005243/en/Survey-by-The-Health-Management-Academy-Reveals-Accelerating-Use-of-AI-to-Overcome-Workforce-Challenges) a pattern in healthcare organizations generally investing in AI in this sequence: solutions for the backoffice, then clinical operations, then clinical care. 


Prioritizing back office first can be seen as a “low risk, high reward” approach by healthcare executives compared to the relative risk of applying AI for clinical care. Plugging AI into a step within revenue cycle management, for example, can provide fast cost savings through freeing up an internal resource. 

‍
Other AI investment is taking place in the front office. Over 27% of healthcare executives pointed to conversational AI as the type of model they deploy.

‍
When asked for what use case, most executives said that they use chatbots. This indicates a knowledge gap between the leaders surveyed and the wide array of additional use cases conversational AI has to offer. 

‍
### Risks of Flawed Approaches to Applying AI
AI is a massive opportunity space in heathcare to be sure, but organizations in the industry are in danger of diminishing ROI or creating even more operational headaches if they don’t apply AI effectively. Two flawed approaches are:

1. Overrelying on point-solutions
2. Leaving humans out of the loop
‍

Point-solutions like chatbot tools can quickly handle high volumes of customer inquiries in the front office, but in healthcare specifically, the risk of providing inaccurate or unhelpful information is much greater. Chatbots need to be trained on specially curated, human-in-the-loop data to ensure they can answer patients’ questions about billing and care. 

‍
Most chatbots in the market today are out-of-the-box, and lack the specific alignment needed to provide patients with truly helpful answers to these inquries. A poorly trained chatbot can alienate patients and harm trust in healthcare providers.

‍
With staff in short supply, healthcare organizations will be tempted to try to apply AI to encompass an entire process. But humans need to stay in the loop. 

‍
According to the survey referenced above, 78% of healthcare executives are actively or planning on applying AI to make revenue cycle management more efficient. They need to define a proper framework for combining AI and human labor in this critical process, or they risk cascading impacts along the revenue cycle due to errors. 

That framework needs to include:

1. Defined steps that are best performed by AI and those best performed by human agents. 
2. Coordinated handoffs of data between electronic health records, AI, RCM platforms, and human agents. 
3. Distinct points in the cycle where human agents QA outputs delivered by AI. 
‍
### AI Enablement for Healthcare Enterprises Made Simple
Applying AI effectively in enterprises is complex. Invisible makes it simple. 


Through our platform, we bring world-class AI models and our expertise from building them into leading enterprises to tackle their greatest challenges. For healthcare, we turn highly coordinated processes like revenue cycle management into ordered, repeatable steps on our platform that are performed seamlessly by AI and our global workforce. 


Processes and steps in our platform are 100% configurable, and we integrate with any system. This allows us to design custom processes involving both AI and people, coordinate data between systems like EHRs and RCM platforms, and minimize errors through human QA along the way. 


For Headway, a mental healthcare company, we use this approach to help match patients with mental health providers at an 8x faster processing speed than previous vendors. We also lowered the cost of the process by 37% vs. their internal team. 


Using our experience in training world-leading AI models, we train healthcare chatbots, too. To optimally align conversational AI models, we hire domain experts in fields like medicine to create AI training datasets. 


Ready to make healthcare simple? [Get in touch](https://www.invisible.co/get-started/).

AI Solutions Alone Don’t Solve Healthcare’s Inefficiency Problem

AI prompt engineer jobs are the hottest jobs in AI. If you're exceptional at prompt engineering, at least [one company](https://www.businessinsider.com/ai-prompt-engineer-jobs-pay-salary-requirements-no-tech-background-2023-3) will pay you a $330k+ salary to do it.
‍

Creating effective prompts for Large Language Models (LLMs) like ChatGPT can be a complex task, as it requires a deep understanding of both the model's capabilities and the task at hand. Here is a quick guide to creating the best prompts for LLMs:
‍

**Be clear and concise**: LLMs perform best when given clear and concise prompts that clearly define the task and provide all necessary information. 


‍**Use natural language**: LLMs are designed to work with natural language. Use it!


**Provide context**: Context ensures that the model can accurately interpret the prompt and generate the desired output.


**Use examples**: Providing examples that are representative of the desired output and provide enough variation ensures that the model can generalize to new inputs.


**Use iterative refinement**: Creating effective prompts often requires an iterative process of trial and error. Start with simple prompts and gradually add complexity and nuance in conversation with your chosen model until the desired output is achieved.


Already a prompt engineering pro but can't meet the scale of prompt engineering support your team needs? [Leave it to the experts](https://www.invisible.co/ai-training/).

The Anatomy of Good Prompt Engineering

A pre-trained large language model (LLM) will only support small-scale generalized tasks. For an enterprise deploying an LLM to perform a specialized task like answering patient inquiries, generic responses aren’t enough.An enterprise-scale use case requires a model that is fine-tuned on a substantial amount of human-generated data specific to that need in order to create value. Otherwise, enterprises deploying generalized models risk overinvesting in a tool that under-delivers.While orchestrating the creation of a fine-tuning dataset is costly and complex, enterprises investing heavily in an AI model can’t afford to not fine-tune it. In fact, <a target="_blank" rel="" href="https://fortune.com/2022/07/26/a-i-success-business-sense-aible-sengupta/">80-90%</a> of enterprise AI projects reportedly fail because they either can’t reach or progress beyond the proof of concept phase, suggesting that models aren’t performing as desired.In this blog post, we explore how LLMs are pre-trained and why fine-tuning is a critical stage in enterprise AI deployment. Let’s dive in.<h2>How LLMs are Pre-Trained</h2>Large language models are pre-trained on billions of unlabeled and unstructured data points. In order to make sense of that data, the model needs to undergo a process of machine learning.There are three main types of machine learning that do this:<ul><li>Supervised Learning: The model learns from provided examples of inputs and their corresponding outputs, much like a student learning from a textbook with solutions.</li><li>Unsupervised Learning: The model finds patterns or groups within the data on its own like a child sorting toys without being told specific categories to sort them into.</li><li>Reinforcement Learning: The model learns by interacting with an environment and receiving feedback (rewards or penalties) for its actions, similar to teaching a pet new tricks with treats or scolding.</li></ul>For an LLM, the overarching goal of these techniques is to create new data or content that is coherent, realistic, and aligns with the characteristics of the training data. Because the training data comes from such a broad source (the internet), the end state constitutes a vanilla model that can only perform generalized tasks.<h3>Why Vanilla Models Need to Be Fine-Tuned</h3>Vanilla models on their own can perform small-scale business tasks like content generation. An enterprise-scale task would instead be a complex process involving multiple points of specialized outputs from a fine-tuned model, which requires the safe handling of proprietary data.Think of a vanilla model as if it were a new hire at your company. It’s unlikely that a new employee would be expected to perform high-value, domain-specific tasks prior to onboarding, training, and on-the-job experience.Like a new hire, a vanilla model falls short of meeting most enterprises' needs right out of the box for the following reasons:<ol><li>Lack of Specialization: Vanilla models are general-purpose and basic. Business problems, however, come with specific challenges and nuances that require specialized knowledge to address those challenges effectively.</li><li>Limited Adaptiveness: While they might work well for basic or introductory scenarios, a vanilla model won’t adapt well to diverse or unseen data. Businesses operate in dynamic environments where data can vary widely, and a model's (and an employee’s) ability to adapt is crucial.</li><li>Lack of Explainability: In business contexts, being able to explain why a specific prediction or classification was made is just as important as the output itself. Stakeholders might need to explain these decisions to others in the organization, customers, or even regulators. Newcomers and vanilla models don’t offer this level of interpretability.</li><li>Compliance Concerns: Especially in sectors like finance and healthcare, models must adhere to industry regulations. Models and people need to be trained compliantly, or else an enterprise could be exposed to the consequences of non-compliance.</li></ol><h4> Where Generalized Models Go Wrong </h4>Let’s break down a practical example using a real business use case. In this scenario, a national pharmacy chain deploys a chatbot, based on a vanilla model alone, on its website and mobile app.The chatbot is designed to assist users in understanding their medication, its side effects, interactions with other medications, and general health advice. Because the model hasn’t been fine-tuned, the pharmacy chain runs these risks:Vanilla LLMs may be able to provide generalized medical information, but can’t accurately represent specific dosages, contraindications, or recent medical findings that illuminate health risks to patients.The model may fail to catch nuance within the context of a user asking about medications or describing symptoms, leading to incorrect or inappropriate medical advice that exposes the company to legal concerns.Foundation models are meant for generalized audiences, and will likely lack the sensitivity needed to appropriately interact with a patient in distress.A foundation model may be more likely to inadvertently misuse a patient’s health data, harming their privacy and exposing the company to even more legal concerns.Realizing that such a model threatens to erode patient trust and do more harm than good, the pharmacy chain may reach a point where they turn away from the model. The solution to avoid limiting the use of these powerful tools is fine-tuning.A fine-tuned model, trained on data produced by experts in the medical field, would comfortably handle patient inquiries and provide better, expert-backed answers. <a target="_self" rel="" href="https://www.invisible.co/get-started/">Speak to our team</a> to learn more about how we fine-tune LLMs for specialized use cases and support enterprises in deploying AI models for complex business processes.

Fine-Tuning is Key To Enterprise AI Deployment

The reality is shifting for enterprises deploying AI. They’re realizing that one Large Language Model (LLM) alone can only perform small-scale tasks, and orchestrating multiple models at once is deeply complex.&nbsp;Making matters worse, most enterprises lack the necessary infrastructure to foster interoperability between the LLMs, people, and tech stack that are critical to performing enterprise-scale business processes. Building that solution now is too costly, <a target="_blank" rel="" href="https://www.invisible.co/blog/fine-tuning-too-costly-and-complex/">too challenging</a>, and it may be too late.&nbsp;With these constraints, innovative companies should explore a solution that incorporates multiple specialized models at once that augment humans in the loop. In this blog post, we’ll go deeper into the limitations of deploying a single LLM, and how multiple models can be steered toward solving real enterprise-level challenges.&nbsp;Let’s dive in.&nbsp;<h2>The Limitations of a Single AI Model for Enterprise</h2>A single AI model will address individual business problems on a small scale, but will either be too generalized or too specialized to address enterprise-scale business problems. Here’s why:&nbsp;Generalized AI models, like GPT or BERT, are designed to perform a wide range of tasks. They're akin to Swiss Army knives in that they are versatile and have broad applicability, but the vastness of their training data and capabilities can make them less efficient or accurate for niche tasks.Specialized AI models on the other hand, like a fine-tuned foundation LLM, are tailored to perform specific tasks with high precision, much like a surgeon's scalpel. While they’re accurate and efficient within their designated domain, they aren’t useful outside of it.&nbsp;Both of these types of models amount to point solutions that enterprise-scale business problems and their processes aren’t typically solved by. Instead, enterprise challenges are multi-faceted and dynamic, requiring an agile solution that orchestrates multiple models at once.&nbsp; <h3>How to Solve the Interoperability Problem</h3>The challenge of combining and integrating models within one system is the downside of repeated technological breakthroughs in the field. New developments are emerging so rapidly that universal standards for how models should cooperate never had a chance to materialize.The status quo, then, looks like this for the foreseeable future: the leading firms use differing model architectures like TensorFlow, PyTorch, or Keras, which have incompatible data formats.&nbsp;<h4>Chaining LLMs Doesn’t Cut It</h4>An emerging framework for getting multiple LLMs to work in concert is by <a target="_blank" rel="" href="https://towardsdatascience.com/a-gentle-intro-to-chaining-llms-agents-and-utils-via-langchain-16cd385fca81">chaining</a> them together. When chained, a series of LLMs can perform more complex tasks, with the input of one model being determined by the output of the model in the previous step.&nbsp;<a target="_blank" rel="" href="https://blog.enterprisedna.co/what-is-langchain-a-beginners-guide-with-examples/">LangChain</a>, for example, is an open-source framework for chaining LLMs to develop workflows and applications beyond what a singular LLM may enable. However, LangChain has <a target="_blank" rel="" href="https://safjan.com/problems-with-Langchain-and-how-to-minimize-their-impact/">well-documented limitations</a>, including: <ul><li>It is inflexible with integrations</li><li>It is prone to breaking as a chain’s complexity increases</li><li>It primarily supports prototypes and demos </li></ul>For an enterprise deploying LLMs, chaining will be insufficient because it doesn’t enable orchestration that includes the people central to a business process. Any high-value use case will require a critical layer of human judgment and quality assurance in conjunction with what LLMs can produce, but the handoff between AI and people isn’t simple.&nbsp; Enterprise leaders should consider how each of these moving parts works in tandem, and whether they have a technology infrastructure that: A) Allows for the interoperability of multiple AI models, and B) Orchestrates their workforce to be augmented by the work done by those AI models. Most don’t.&nbsp;To learn more about how LLMs work and how Invisible delivers maximal alignment, watch this: <div data-youtube-video=""><iframe width="640" height="480" allowfullscreen="true" autoplay="false" disablekbcontrols="false" enableiframeapi="false" endtime="0" ivloadpolicy="0" loop="false" modestbranding="false" origin="" playlist="" src="https://www.youtube.com/embed/CTNVmBBw80U" start="0"></iframe></div><h4>Interoperability in Practice</h4>Here’s an example of a business use case that illustrates the importance of interoperability between AI models and people. An international bank that operates in 50 countries is likely regulated in 50 different ways.&nbsp;That bank, like any other, is at constant risk of fraud attempts. Each country they operate in has a unique regulatory framework for how the bank can detect and thwart these attempts.&nbsp; The bank decides to deploy multiple AI models, specialized for specific tasks, in order to mitigate risk and stay compliant. Their success relies on a system in which tasks are handed off seamlessly between an AI model and people providing human intelligence.&nbsp;Let’s look at the process flow:&nbsp; Step 1: Initiation:<ul><li>A transaction is initiated anywhere within the bank’s international network.</li></ul>Step 2: Regulatory Understanding:<ul><li>Model A: Interprets local financial regulations and translates them into actionable rules for the transaction.&nbsp;</li></ul>Step 3: Transaction Analysis &amp; Fraud Detection:<ul><li>Model B: Monitors the transaction in real-time, profiling it based on risk using deep learning.</li><li>Model C: Examines the transaction against regional fraud patterns and flags suspicious activity.&nbsp;</li></ul>Step 4: Human Review:&nbsp;<ul><li>Suspicious transactions flagged by Models B and C are automatically sent to regional compliance officers for review. These officers, using their local expertise, make the final decision on the transaction's legitimacy.</li></ul>Step 5: Feedback Loop:<ul><li>Post-human review, results are fed back into Model B and Model C, refining their detection capabilities.</li></ul>Step 6: Report Generation &amp; Audit:<ul><li>Model E: Consolidates the decisions made, reasons for suspicion, and final outcomes into a report for internal audits and regulatory bodies.</li></ul>Step 7: Conclusion:&nbsp;<ul><li>Transaction is either processed or halted based on AI analysis and human review.</li></ul>An overarching platform integrates the outputs from all models involved in this process. It schedules tasks, ensures real-time communication between models, incorporates human decisions, and triggers appropriate actions, like halting a transaction.<h3>How Invisible Enables Enterprise AI Deployment</h3>Our process orchestration engine is the ideal platform to support AI interoperability that also augments human input. In conjunction with our global workforce of trained operators, we enabled a <a target="_blank" rel="" href="https://www.invisible.co/results/">Big 4 retailer</a> to leverage multiple AI models to solve a uniquely complex, large-scale business problem.&nbsp;The client attracted over 50 million online visitors per month but had huge volumes of third-party product listings that contained missing and erroneous data fields that prevented thousands of products from appearing on search engines. This meant they couldn’t compete with the biggest player in the space, Amazon.&nbsp;Invisible applied generative AI to perform the heavy-lift data enrichment steps at an unparalleled scale. Another model flagged data containing errors, which were resolved by a dedicated team of operators who applied a finer touch.&nbsp;Our client saw a near-immediate 9x ROl. It took just 30 days.&nbsp;The next 10,000 products outperformed in their category by 140%. Overall, 44% of SKUs entered page one for search results, increasing both visibility and sales velocity.Ready to solve your complex business problem for good? See more about how we enable enterprise AI deployment <a target="_blank" rel="" href="https://www.invisible.co/ai/">here</a>.&nbsp;

One LLM Alone Can’t Solve Enterprise Business Problems

In today’s market, we see three major challenges standing in the way of enterprises seeking to leverage AI for a competitive edge: <ol><li>Talent with AI expertise is essential to building and using AI within the enterprise.</li><li>AI talent is tough to come by.&nbsp;</li><li>AI talent is costly.&nbsp;</li></ol> In fact, <a target="_blank" rel="" href="https://www.zdnet.com/article/more-skills-needed-to-help-ai-plug-skills-gaps/">67% of IT leaders</a> named the AI skills shortage as the number one barrier to deploying AI, according to Rackspace Technology. Yet recent AI-related job postings from July are <a target="_blank" rel="" href="https://www.computerworld.com/article/3702711/ai-skills-job-postings-jump-450-heres-what-companies-want.html">450% YoY</a>, highlighting that this hasn’t stopped them from trying.&nbsp;&nbsp; To further counter this shortage, enterprises are shifting talent within traditional technology and data roles to AI-related priorities, implementing AI point solutions to replace customer service representatives, and leveraging the flexibility of freelancers in a move that is shoring up the gig economy. But none of these have delivered the outcomes enterprises are looking for.&nbsp; Instead, the clearest path to ROI may lie in a strategic partner that merges the best of human and artificial intelligence within a single end-to-end technology solution. Let’s dive in.&nbsp; <h2>Why AI Expertise is Essential</h2>An effective enterprise AI solution can’t simply be patched together. To understand why, let’s look at the varying degrees of AI adoption within an enterprise.&nbsp; Many models depict enterprise AI adoption across three levels, with AI integration deepening within an enterprise’s operations at each step. <a target="_blank" rel="" href="https://www.egonzehnder.com/industries/technology-communications/artificial-intelligence/insights/is-your-c-suite-prepared-for-artificial-intelligence">Egon Zehnder</a> is one such organization. Here’s our take, including what adoption looks like at each stage:&nbsp; Level 1: AI is introduced in an organization<ul><li>A team, or teams, onboard a point solution like an AI-powered SaaS tool.</li><li>AI is added as a feature to a product or service.</li><li>A core AI solution is not centralized within the organization’s operations. </li></ul>Goals: Automate and increase the efficiency of repetitive low-complexity tasks, or improve the customer experience.&nbsp; Level 2: Business processes become centered in AI&nbsp;<ul><li>A foundation LLM is customized with fine-tuning to support one or more business functions.</li><li>Data science teams begin to shift focus toward maximizing the AI model’s value.</li><li>AI becomes a core product feature. </li></ul>Goals: Increase the efficiency and quality of an entire business function’s output and optimize value-creation for a key product.&nbsp;&nbsp; Level 3: An organization is fully AI-enabled&nbsp;<ul><li>An LLM or multiple LLMs working in concert deeply supports a core business process and has an impact across the entire organization.</li><li>Humans in an organization work seamlessly in tandem with the AI and the rest of their tech stack.</li><li>Existing business processes are redesigned to be AI-enabled; new business processes are structured around AI. </li></ul>Goals: Engage on every opportunity, maximize business productivity and outcomes, and widen the gap with competitors. Off-the-shelf AI tools tend to cater to enterprises at Level 1. But as enterprises scale to Levels 2 and 3, costs and complexity begin to mount. That’s because, at each level of adoption, the expertise and resources required scale exponentially. Based on our experience working with the leading AI model development firms and enterprises deploying AI, here is our assessment of the organizational requirements for Levels 2 and 3: Level 2 RequirementsAt this stage, an AI model is integrated within an enterprise’s operations at a larger scope. The stakes for having an accurate and robust solution are higher, so enterprises need:&nbsp;<ul><li>Skilled AI trainers to produce preference data for Supervised Fine-Tuning and perform model evaluations.</li><li>Trained experts to monitor data quality and manage the AI trainer workforce.</li><li>Tech infrastructure and data scientists to manage data securely and efficiently.</li><li>Properly structured and enriched proprietary data.</li><li>A <a target="_blank" rel="" href="https://www.forbes.com/sites/rashishrivastava/2023/09/01/ai-red-teams-google-nvidia-microsoft-meta/">red team</a> to uncover hallucinations and build feedback loops that steer a model toward aligned behavior.</li><li>Process mining and experience in shaping business processes around an AI model and human intelligence.</li><li>Tech infrastructure that moves data cleanly between AI, people, and the organization’s tech stack.</li></ul>Use case example: A healthcare firm utilizing a conversational AI tool that integrates with EMRs and is fine-tuned on preference data from domain experts.&nbsp; Level 3 RequirementsWhen enterprises reach this level of adoption, they are shaping business-critical processes around one or more AI models working in concert. They need:&nbsp;<ul><li>All of the requirements of Level 2.</li><li>A technology platform capable of orchestrating one or more AI models, software integrations, and humans in the loop to maximize efficiency and value.</li><li>Research scientists with expertise in measuring.</li></ul>Use case example: A financial institution deploying <a target="_blank" rel="" href="https://www.invisible.co/blog/one-llm-not-enough/">multiple generative AI models</a> to improve fraud detection response accuracy and speed For a business restructuring its operations around AI, the requirements listed above necessitate either a monumental shift in internal resources or a recruiting effort of similar cost and magnitude. Both routes threaten ROI due to their <a target="_blank" rel="" href="https://www.invisible.co/blog/fine-tuning-too-costly-and-complex/">expense and complexity</a>.&nbsp; <h3>Why Choose a Strategic Partnership</h3>The strongest alternative is a partner experienced in AI model training and deployment. While many claim to transform enterprises with AI, few offer an end-to-end solution— this is where Invisible steps in. Invisible works alongside global enterprises, enabling them to leverage the full potential of AI through our process orchestration platform: a potent blend of a global team of experts, cutting-edge automation capability, and AI.&nbsp; This platform enables:<ul><li><a target="_blank" rel="" href="https://www.invisible.co/blog/fine-tune-ai/">Customizing AI models</a> for specialized enterprise use cases.</li><li>The orchestration of multiple models and humans in the loop to deliver powerful outcomes through large-scale business operations.</li></ul> Invisible’s value proposition lies in our deep relationships with the world’s leading AI model development firms, for whom we provide human-in-the-loop data training that delivers more accurate and helpful models.&nbsp; This unique position grants our enterprise partners a head start in their AI journey with access to the latest thinking in AI and deep expertise in the most powerful models, allowing us to craft solutions that maximize model efficiency, while negating the need for additional recruitment, team shifts, or tech investments, ensuring businesses are AI-enabled and ROI-focused. <h4>Case Study: Strategic Partnership with a Big 4 Retailer</h4>A retail giant required a precise, high-throughput solution for enhancing product descriptions for its online marketplace. Cluttered third-party data obscured listings from search engines, leaving millions in revenue on the table.&nbsp; Invisible crafted a dual-AI solution combined with human expertise in which one AI model enriched data from scraped content, while another identified and flagged errors. Errors were then refined by human experts. The outcome? Invisible enriched a vast product range swiftly, surpassing other solution providers. The retailer witnessed a staggering 9x ROI within a month. Ready to harness AI's power for your enterprise? Discover how Invisible can be your strategic partner <a target="_blank" rel="" href="https://www.invisible.co/ai/">here</a>.

Overcoming the AI Talent Scarcity Problem Requires a New Approach for Enterprises

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