Prompting Best Practices (Claude Platform Docs)
by Anthropic · Anthropic
Anthropic's continuously updated prompting reference, organized as per-model behavioural guidance, then techniques that apply to all current models - clarity, examples, XML structuring, extended thinking, tool use, agentic loops - then migration notes for older prompts.
More resources on Prompt Engineering
Anthropic Learn
Anthropic's free learning hub, now Claude Academy, with self-paced courses and tutorials on AI fluency, model capabilities and limits, prompt writing, and building applications with the Claude API. Learners can write clearer prompts, judge model output critically, and start developing with Claude.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Shows that prompting a large model with a few worked examples containing intermediate reasoning steps substantially improves arithmetic, commonsense, and symbolic reasoning, and that the effect emerges only at sufficient model scale. The origin of chain-of-thought prompting.
Prompt Engineering Guide
Reference covering prompting techniques with worked examples: zero-shot and few-shot, chain-of-thought, self-consistency, ReAct, and tree of thoughts, plus model-specific notes and adversarial prompting risks. Readers finish able to choose techniques deliberately.
Prompt Engineering for LLMs: The Art and Science of Building Large Language Model-Based Applications
Two engineers who built GitHub Copilot explain why prompts work in terms of how models complete text, then build up few-shot patterns, chain-of-thought, retrieval-assembled context and evaluation for production applications rather than one-off chat sessions.
Effective Context Engineering for AI Agents
Anthropic's applied AI team reframes prompting as one part of curating the whole context window. Covers context rot, calibrating system prompt specificity, minimal non-overlapping tool sets, just-in-time retrieval, and compaction, note-taking and sub-agents for long-horizon tasks. This is the resource that answers the topic's hardest question - where prompting stops and retrieval, memory or architecture must take over. It gives concrete mechanisms (compaction, structured note-taking, sub-agent isolation) rather than the vague 'context is the new prompt' takes that flooded blogs afterwards.
GPT-5.2 Prompting Guide (OpenAI Cookbook)
OpenAI's own guide to steering its current flagship reasoning model: controlling verbosity, preventing scope drift, reasoning-effort settings, long-context handling, tool-call parallelism, schema-driven extraction from documents, and a migration table from earlier models. Reasoning models broke a lot of received prompting wisdom, and this is the clearest first-party account of what changed.