Topic
Chain-of-Thought Prompting
7 posts on chain-of-thought prompting — part of glossary on the n4n AI blog.
What is chain-of-thought prompting?
Chain-of-thought prompting forces models to show intermediate reasoning steps, improving accuracy on complex tasks without fine-tuning.
How chain-of-thought prompting improves math accuracy
Chain-of-thought prompting forces models to show their work, dramatically improving math accuracy by making reasoning explicit and verifiable.
Few-shot chain-of-thought examples that actually work
Practical few-shot chain-of-thought examples for math, code, logic, and multi-hop reasoning with copy-paste templates.
Chain-of-thought vs reasoning models: do you need both
A practitioner's comparison of chain-of-thought prompting versus built-in reasoning models across cost, latency, control, and reliability — with a decision framework for your use case.
Chain-of-thought prompting on GPT-5 vs Claude
Compare chain-of-thought prompting on GPT-5 vs Claude 3.5 across reasoning depth, tool use, latency, and cost — with a verdict by use case.
Chain-of-thought prompting explained with real examples
A practical guide to chain-of-thought prompting with working code examples, common failure modes, and tradeoffs engineers face in production.
Chain-of-thought prompting: a step-by-step guide
A practical chain of thought prompting guide for engineers building LLM applications, with code patterns, tradeoffs, and common pitfalls.
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88