State-of-the-art large language models (LLMs) have made significant progress in natural language processing (NLP), allowing artificial intelligence models to generate human-like text. However, the performance of earlier models such as GPT-3 declines when they encounter complex logical reasoning or multi-step problem-solving.
A study evaluating LLMs on multi-step logical reasoning tasks found a significant performance drop as reasoning depth increased, from around 68% at depth-1 to about 43% at depth-5. Increasing the model size does not resolve the issue since even LLMs struggle with complex problems such as arithmetic and logic tasks when answering in one step, though smaller models tend to have more issues with reasoning.
So, what steps can we take to connect the ability to sound intelligent with genuine intelligence? Chain of thought (CoT) reasoning helps AI systems solve complex tasks by breaking them into logical steps that mimic human cognitive processes. It improves LLM's reasoning by requiring an explanation before final answers.
This article explores reasoning’s role in advancing generative AI. It explains how CoT prompting aids genAI models in solving complex tasks by mimicking human reasoning. We’ll also discuss challenges in creating reasoning datasets and emphasize enterprise application use cases.
LLMs are designed to generate the next word based on patterns in language data. The autoregressive generation system works well for fluent language, but the model cannot plan structured logical thoughts. Multiple problems emerge when there is no established reasoning framework in place. Some include:
Reasoning includes breaking down problems into steps, maintaining context, and ensuring logical consistency. When LLMs engage in a complex reasoning process, they transition from basic text generators into problem-solving systems. Combining multi-step deduction with deeper contextual understanding enables LLMs to address problems that earlier models had difficulty solving.
Reasoning supports the generation of clear, logical explanations, incorporating self-checks and managing intricate, text-based queries. The CoT reasoning technique generates an entire logical argument with premises and a conclusion in response to the original prompt, improving the reliability and model's decision.
Chain of thought reasoning is the model's ability to generate intermediate reasoning steps that lead to an answer or conclusion. The CoT reasoning method guides models in breaking down problems into logical steps, aligning with human problem-solving methods when working through complex questions.
Humans process problems through sequential thinking during problem-solving activities, including mathematical computations, math word problems, legal analysis, and strategic planning. When solving a math problem like 24 × 17, most people don’t recall the answer instantly. Instead, they break it into easier steps:
Step 1: Split the problem
24 × 17 = (24 × 10) + (24 × 7)
Step 2: Solve the parts
24 × 10 = 240
24 × 7 = 168
Step 3: Add the results
240 + 168 = 408
The systematic method helps users maintain accuracy while allowing them to detect errors during each operation phase. LLM without complex reasoning capabilities would produce responses by predicting outputs from training data patterns, increasing the likelihood of incorrect answers.
Take logical reasoning as an example. A model with CoT reasoning would be necessary to determine whether or not all roses are included in the flower group in the following example.
In the absence of CoT, the model may try to give a direct response and incorrectly conclude that all roses fade due to exposure to specific sentence structures in the model's training data.
One of the most proven methods for triggering chain-of-thought reasoning in AI systems involves CoT prompting, or prompt chaining, over standard prompting. Users can achieve better results by directing models through structured prompts that guide the model to provide step-by-step explanations. The accuracy of model responses improves significantly when CoT prompt engineering requires models to explain steps in logical deduction, arithmetic, and multi-step reasoning, including complex reasoning tasks.
Chain-of-thought prompting works by formulating the input so the model is encouraged to produce a reasoning process in a series of intermediate reasoning steps followed by the answer. Instead of giving a solution in one go, the model is guided to deliver it through a sequence of smaller, easily digestible steps.
Chain-of-thought prompting elicits reasoning by substantially improving performance when tackling problems requiring multiple thinking steps, including reasoning and multi-hop question answering. To understand the difference between a direct prompt and a CoT prompt, let's look at a simple example:
Direct prompt: Calculate 17 × 12.
Model’s answer: 204 (could be right or wrong, no explanation)
Chain of Thought Prompt and Answer:
The model’s answer to the chain of thought prompt is clear, step-by-step, and correct.
The model is quick with each instructed prompt. However, there are cases when the method to get to the answer is just as important as the answer itself, like during legal arguments, scientific inquiries, or financial planning.
Using chain-of-thought prompting produces substantial performance improvements across various reasoning-intensive operations. These include:
The direction of model reasoning through CoT prompting helps prevent hallucinations while making results more accurate.
Chain-of-thought prompting has limitations, so it is best used with other techniques. Some of the limitations include:
AI requires proper training data beyond prompts to learn reasoning capabilities. A high-quality reasoning dataset shows the complete thought process rather than only providing final solutions. COT represents the same concept as showing your work in mathematics classes.
Creating such data requires substantial work. The process demands experts to compose thorough solutions or models that undergo rigorous solution verification. It takes time but is the fundamental method for developing intelligent AI systems.
Effective reasoning in AI depends on the quality, structure, and diversity of the data used during training. Now, let’s discuss the data requirements for reasoning.
A dataset cannot simply contain a set of inputs and outputs; it needs to encompass all reasoning steps. Each step of reasoning needs to be logically connected to the one preceding it and relevant to the set problem.
Hallucinations, vague transitions, and circular reasoning should not occur. These annotated steps offer a sequential breakdown of problems and can be employed in supervised training to equip the model with stepwise reasoning.
A high-quality reasoning chain requires complete logical alignment between steps that smoothly guide toward the correct answer. Mathematical reasoning follows strict formal rules, while common-sense tasks follow a more descriptive approach. The model's reliability decreases when it gives the incorrect answer through an inaccurate or illogical chain of intermediate steps.
Data organization and the training data structure influence reasoning performance. Domain-specific datasets rely heavily on domain knowledge and follow established reasoning frameworks. Such information processing specialization leads to high accuracy in the area.
In comparison, general reasoning datasets focus on developing versatile problem-solving abilities through diverse tasks, including word problems, logic puzzles, and hypothetical scenarios. The datasets support knowledge transfer between different domains. Models become more adaptable when they integrate task-specific precision with general problem-solving breadth.
Creating effective CoT training data requires more than just examples, it needs structure, clarity, and relevance. Below, we outline key best practices for ensuring high-quality, reasoning-focused datasets.
LLMs expand training data by creating reasoning examples that humans or automated systems refine or filter. A recent approach, Active Prompting, interacted with an LLM several times to obtain different responses and then used uncertainty measurement to choose which cases needed human analysis. The chains were then verified and added to form a semi-automated pipeline that generates efficient CoT data with reduced effort.
Chain-of-thought outputs tend to generate lengthy responses, which increase computational costs and potential errors. The Sketch-of-Thought (SoT) framework, based on cognitive science principles, helps models create brief reasoning sketches that mimic expert step-by-step outlines.
SoT implements linguistic constraints and shorthand abbreviations to reduce token usage by 76% without compromising accuracy. Structured reasoning templates like:
This step-by-step approach keeps responses clear, concise, and logical. When designing training data or prompts, structured reasoning templates help models stay on track, improve consistency, and make outputs easier to evaluate.
Tasks involving complex puzzles, multi-hop planning, and long proofs require extensive reasoning chains. Step-by-step linear procedures tend to become complex and lose their logical flow. Advanced prompting frameworks like Tree-of-Thoughts (ToT) can perform backtracking while comparing different solution paths to select the most optimal choice.
ToT includes self-assessment pruning, allowing the model to identify promising solutions before moving forward with evaluation. So, when training the model, we shouldn’t only show it the final correct answer. We should also show how to think through and compare different options. This makes the training more complex, but it's important for solving advanced problems.
Tasks requiring multiple decision points should be structured as non-linear processes. Decision-rich problems require hierarchical or tree-structured reasoning methods to achieve superior performance.
Larger models can develop reasoning capabilities that function independently from CoT prompts, known as zero-shot chain-of-thought. GPT-4 and similar large models generate step-by-step answers automatically because their training involves explanatory Q&A content.
RLHF represents a different approach to generating CoT behavior. A base model trained through RL on reasoning benchmarks developed step-based problem-solving. Reasoning abilities develop through feedback mechanisms beyond traditional supervision methods.
Supervised fine-tuning on CoT data followed by RLHF helps the model refine its reasoning to match human standards of validity and transparency. The best practice for large models is to combine their size with step-by-step training and human feedback to improve their reasoning skills.
Developing reliable CoT datasets improves LLMs' reasoning ability. However, it also comes with challenges. Some include:
Developing CoT datasets needs experienced AI operations teams and human data trainers who grasp both the scientific and cognitive aspects of reasoning. Their expertise guarantees that datasets are cognitively pertinent and aligned with human thinking patterns.
Business applications greatly benefit from LLMs with reliable reasoning abilities in operational environments. The gap between standard responses and rational explanations determines whether users rely on AI systems in critical situations. Let’s analyze two problems where CoT reasoning solves everything.
Analysts analyze financial reports to identify anomalies, evaluate investment risks, and project future market trends. The absence of clear reasoning paths makes it difficult to trust model outputs. The CoT framework supports AI in explaining revenue declines step by step, linking them to market changes and historical trends. Here’s how it helps:
One example is FinRobot, an open-source AI agent platform using a multi-agent CoT system to replicate human analyst reasoning during equity research and valuation. This system combines quantitative and qualitative methods to generate complete financial analysis results.
Legal teams handle contracts, policies, and regulations that include hidden obligations and potential risks within their lengthy text. The systems should interpret context, compare clauses, and identify missing or conflicting terms with supporting evidence. The CoT framework helps in:
Allen & Overy (A&O) has improved its global operations by implementing Harvey, an advanced AI platform for enhancing legal practice. Implementing Harvey's capabilities at A&O helps lawyers optimize their legal processes for contract analysis, due diligence, litigation support, and regulatory compliance.
Reasoning is key in advancing next-generation AI models, helping them move beyond simple pattern recognition to handle more complex tasks. Chain-of-thought reasoning improves model outcomes by solving problems through sequential steps.
However, developing the right training data remains challenging. Effectively reflecting real-world scenarios requires careful design, deep domain knowledge, and sensitivity to context. Organizations seeking to use LLMs for applications should establish reasoning capabilities instead of treating reasoning as an additional functionality.
An experienced AI training partner can bring proven techniques to train and test models with strong reasoning and simplify everything from fine-tuning to deployment. Invisible has been trusted to train 80% of the world’s leading AI models, and delivers faster deployment and more reliable performance in the evolving AI space. Request a demo to learn more.
