Chain of thought

What it is

When you use this technique, you instruct the model to think step by step, often by appending a phrase like 'Let us think step by step' to your system prompt. This forces the model to perform a sequential generation process where every token produced acts as an intermediate scratchpad. Because LLMs are autoregressive, these preceding reasoning tokens are included in the model's self-attention scope for subsequent tokens. This process effectively expands the computation time per query, often increasing total token consumption by 2x to 10x depending on the complexity of the task.

Why it matters

Ignoring this technique leads to lower accuracy on complex logic, math, or multi-step classification tasks where a direct answer is prone to hallucination. For engineers, it directly impacts your latency budget and token costs, as the model must generate significantly more output before completing the request. Deciding when to enforce chain of thought versus a direct response is a critical lever for balancing request cost against result quality in production features.

In practice

You implement this in code by modifying your system or user message templates to include reasoning triggers. In production monitoring, you will notice increased latency and higher token usage per request, which may require adjusting your request timeouts or budget caps for specific endpoints.

The tradeoff

The primary tradeoff is higher latency and increased cost per request in exchange for improved logical accuracy on difficult problems.

Where it appears

Research summaries that use Chain of thought, each linked to its source paper.

• Improving Multimodal Retrieval with Reasoning
  Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval

  UniME-R1 enhances multimodal retrieval performance by integrating retrieval-centric chain of thought reasoning to correct errors where standard embedders fail.

• Efficient Spatial Reasoning for Autonomous Driving
  MoRAL: Sensor-Grounded BEV Reasoning for Compact VLMs toward Edge-Oriented Autonomous Driving

  MoRAL is a compact vision-language model architecture that uses a deterministic bird's eye view pipeline to improve spatial reasoning and emergency braking safety in autonomous driving tasks.

• Stopping Visual Forgetting in Multimodal Models
  Remember-R1: Mitigating Long-Context Visual Forgetting through Reinforcement Learning

  Remember-R1 uses reinforcement learning to force multimodal models to maintain focus on visual evidence throughout long reasoning sequences.

• Improving AI Tool Use for Small Models
  Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation

  The researchers created a framework that generates high quality synthetic data to help small language models accurately perform tasks involving external software tools.

• Structuring LLM Thinking Through Tree Search
  Tree of Thoughts: Deliberate Problem Solving with Large Language Models

  The paper introduces the Tree of Thoughts (ToT) method, which allows language models to perform strategic planning and lookahead by exploring multiple reasoning paths instead of a single linear chain.

• Balancing Faithfulness and Safety in Reasoning
  Risky Business: Measuring The Faithfulness-Safety Tension

  The researchers developed a method called Targeted Reasoning Replacement to measure and mitigate the conflict between keeping a model's reasoning process transparent and ensuring it remains safe from harmful inputs.

• Teaching Robots Using Internet Vision Knowledge
  RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

  The RT-2 model improves robot control by fine-tuning vision-language models on robotic trajectory data alongside large-scale web-based datasets.

• Improving Large Language Model Reasoning Capability
  Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

  The researchers show that prompting large language models with a sequence of intermediate reasoning steps significantly improves their performance on complex tasks.