How LLMs think step by step & Why AI reasoning fails

2026-01-05 · Source: What's AI by Louis-François Bouchard · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Chatbots often struggle with complex, multi-step questions, leading to reasoning failures. A common prompt engineering technique, "chain of thought" (CoT), addresses this by instructing the model to "think step by step," which forces it to outline definitions, compare concepts, and then conclude, significantly improving accuracy. Newer, advanced reasoning models, such as Google Gemini 2.5 Pro, OpenAI's GP5, and Claude Opus, integrate this step-by-step thinking internally, automatically generating a logical thought process without explicit prompting. This intrinsic capability allows these models to handle intricate problems more accurately, demonstrating a shift towards more robust internal reasoning mechanisms in large language models.

Key takeaway

For prompt engineers designing complex chatbot interactions, understanding and implementing Chain of Thought (CoT) prompting is crucial to mitigate reasoning failures. Your prompts should explicitly guide models to break down multi-step problems, or you should consider leveraging advanced models like Gemini 2.5 Pro or Claude Opus that integrate this capability internally, ensuring more accurate and reliable responses to intricate user queries.

Key insights

Chain of Thought prompting and advanced models enhance chatbot reasoning for complex, multi-step queries.

Principles

• Step-by-step thinking improves reasoning accuracy.
• Internal thought processes reduce logical errors.

Method

Chain of Thought (CoT) involves adding "let's think step by step" to a prompt, guiding the model to generate intermediate logical steps before providing a final answer, thereby increasing reasoning accuracy.

In practice

• Use CoT for multi-step chatbot questions.
• Evaluate advanced models for complex reasoning tasks.

Topics

• Reasoning Capabilities
• Chain of Thought
• Prompt Engineering
• Large Language Models
• AI Hallucinations

Best for: Prompt Engineer, AI Engineer, Machine Learning Engineer

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• Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs — Chain-of-Thought prompting degrades visual spatial reasoning in multimodal LLMs due to shortcut learning and hallucination.
• Where Do Large Language Models Fail on Competitive Programming? A Taxonomy of Failures by Algorithm Type and Difficulty Rating — Chain-of-Thought prompting can degrade LLM code generation performance and instruction adherence in competitive programming.
• Why We Think — Allocating more test-time compute via methods like Chain-of-Thought significantly boosts LLM reasoning and problem-solving capabilities.
• Prompted CoT Early Exit Undermines the Monitoring Benefits of CoT Uncontrollability — LLMs can bypass CoT monitoring by displacing reasoning into the controllable response channel with minimal accuracy loss.

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Editorial summary, takeaway, and curation by AIssential. Original article published by What's AI by Louis-François Bouchard.