The core idea: Good AI output starts with good input. Context engineering is the practice of deciding what the model should know before you ask it to work.
- What Is Context Engineering? The pillar guide: what context engineering means and why it matters more than prompt wording for real work.
- Context engineering vs prompt engineering Learn the difference between context engineering and prompt engineering, and why source selection often matters more than clever wording.
- Context window sizes by AI model A practical guide to context window sizes for major AI models, with token budgeting advice for Claude, GPT-4o, Gemini, Perplexity, and local models.
- Why more context can make LLMs worse More context is not always better. Learn why irrelevant files, stale notes, and duplicated text can make AI answers less accurate.
- How to budget tokens properly Learn a practical token budgeting workflow for files, prompts, and expected answers across Claude, ChatGPT, Gemini, and local LLMs.
- The llms.txt standard explained A plain-English explanation of llms.txt, what belongs in it, and how to prepare AI-readable site context without dumping everything.
- Static context vs RAG vs in-chat upload Compare static context bundles, retrieval-augmented generation, and direct file uploads so you can choose the right AI context workflow.
- How to write a CLAUDE.md or AGENTS.md file Learn what belongs in CLAUDE.md and AGENTS.md so AI coding agents understand project rules, style, workflows, and constraints.
- Context rot Context rot happens when old files, stale decisions, and outdated docs quietly poison AI output. Learn how to spot and prevent it.
- Reusable AI context files Learn how to build reusable AI context files that work across Claude, ChatGPT, Gemini, Perplexity, and local models.
- Context engineering for codebases Learn how to select codebase files for AI without dumping the entire repository into the context window.
- Context engineering for research Build better AI research sessions by curating papers, notes, annotations, and source material before asking for synthesis.
- Context engineering for client work Package client briefs, notes, websites, decks, and docs into reusable AI context without rebuilding from scratch.
- AI memory vs project context AI memory and project context solve different problems. Learn when to rely on memory, projects, and explicit context bundles.
- Context templates vs prompt templates Prompt templates format the request. Context templates define the source package. Learn when each one matters.
- Context compression and minification Learn when to compress, minify, summarize, or remove files from an AI context bundle.
- How to evaluate context quality Use a simple checklist to decide whether an AI context bundle is clear, relevant, current, and ready to use.
- Multi-model context portability Learn how to build AI context that works across Claude, ChatGPT, Gemini, Perplexity, and local LLMs.
- Local-first context engineering Build AI context locally before deciding which AI tool should receive it.
- Building a source-of-truth AI bundle Create a source-of-truth AI bundle that captures current project facts, constraints, and source files without drifting into clutter.
- What Is a Context Window? A plain-language explanation of AI context windows, token limits, and how to make the most of them.
- What to Include in an AI Context Bundle A practical guide to the four components of an effective AI context bundle.
- Feed Multiple Files to Claude Merge multiple files and folders into one upload for Claude. A practical guide to building file-based AI context.
- Give ChatGPT Context From Files Step-by-step guide to uploading local files and folders to ChatGPT for better AI responses.
- Stop Re-Explaining Your Project to AI Build a reusable context bundle so AI instantly understands your project at the start of every session.
- Same Context in ChatGPT and Claude Build a portable AI context bundle that works across ChatGPT, Claude, Gemini, and any other AI platform.