Context Engineering

Understand how context engineering reduces API costs by applying minimal-context prompting, summarization, and model tiering for efficient token use.

Learn prompt and context engineering basics. Optimize prompts by trimming bloated inputs to relevant snippets and measure token savings with examples.

Learn to trim prompt context for code tasks using dependency graphs. Discover how to build a minimal Python dependency graph and select relevant files.

Condense long documents for Claude using a cheaper model first. Learn how this two-step summarization pattern saves costs and when to apply it.

Learn why adding unnecessary context degrades AI answer quality and increases cost, a phenomenon known as context rot. Discover how to optimize prompts.

Optimize model costs by matching task complexity to the right Claude tier. Learn how to use a checklist to decide between Haiku, Sonnet, and Opus.

Learn best practices for prompt and context engineering to reduce token spend and maintain answer quality. This guide covers minimizing sent data and managing conversation

A single-page roundup of every highlight bullet from the 10 pages in the Context Engineering section, grouped by source page so you can scan all 56 takeaways without opening each article individually.