Turning LLMs from Liars into Experts
Context Engineering in Practice
Context Engineering in Practice | RAG · MCP · CLAUDE.md · Agentic RAG, benchmarked end to end
Overview
Why does the same question give wildly different answers? The cause isn't your prompt — it's your context. Across three fictional internal tools, this book runs original benchmarks proving that context strategy moves answer quality by up to 4.6x. Larger models lie more convincingly. Small model + RAG outperforms a large model alone. From those findings the book builds the full Context Engineering system: 5-stage strategy, RAG, MCP server design, CLAUDE.md, and Agentic RAG.
What you will be able to do
- Master a 5-stage context strategy that lifts answer quality by 2.2x or more
- Understand why RAG accounts for 80% of the gain — and where the breakthrough point lives
- Design and operate an MCP (Model Context Protocol) server
- Apply staged CLAUDE.md design patterns to optimize project context
- Implement Agentic RAG in Python end to end
Who is this book for
- [Intermediate engineer] Looking for the next step beyond prompt engineering
- [LLM evaluator] Trying to choose between RAG and MCP with confidence
- [Hallucination wrangler] Frustrated that even large models still get things wrong
- [Claude Code user] Want to learn staged CLAUDE.md design
- [AI agent builder] Need to implement Agentic RAG in production
- [Benchmark-driven] Want quantitative comparisons between context strategies
Problems this book solves
- Prompts are tuned but answer quality still swings
- RAG is implemented but it's unclear whether it's actually working
- Can't tell when to reach for MCP servers vs plain RAG
- CLAUDE.md is in the repo but it's unclear what to put in it
- Heard of Agentic RAG but unsure how it differs from regular RAG
- Switching LLMs keeps changing answer quality unpredictably
Where this book stands
- Benchmark-first (a 4.6x quality gap proven by original experiments)
- Context Engineering specialist (a separate axis from prompts and harnesses)
- Intermediate level (assumes you've used an LLM before — not an RAG primer)
- Code-included (96 production-quality Python files published on GitHub)
Why this book
- Original benchmarks prove that context strategy moves quality by 4.6x
- Shows experimentally that larger models lie more convincingly, and that small model + RAG beats a large model alone
- Covers RAG, MCP, CLAUDE.md, and Agentic RAG in a single coherent volume
- 96 production-quality code files on GitHub, fully reproducible
- Connects directly to Claude Code via staged CLAUDE.md design
How this differs from other AI books
| Compared to | This book's difference |
|---|---|
| Prompt engineering books | Focuses on the layer below prompts — context design. Picks up where prompt engineering ends. |
| RAG primers | Goes beyond RAG alone, integrating RAG, MCP, CLAUDE.md, and Agentic RAG into one Context Engineering system. |
| Vendor official documentation (OpenAI, Anthropic, etc.) | Original benchmarks show how much things actually change — quantitatively, not qualitatively. |
Table of contents
- 01 Cover Free preview
- 02 Introduction Free preview
- 03 Five Answers — the same question, five patterns Free preview
- 04 LLMs Lie — the anatomy of hallucination
- 05 How Context Engineering Began
- 06 First Steps — from zero-shot to strategy
- 07 Few-Shot — examples that lift quality
- 08 RAG — the technique that owns 80% of the gain
- 09 Full Context Engineering — integrating the 5 stages
- 10 MCP — Model Context Protocol server design
- 11 Memory — context that persists
- 12 (continues — 22 chapters plus Appendix A)
The same question keeps giving you wildly different answers. The cause isn’t your prompt. It’s your context.
This book runs original benchmarks across three fictional internal tools and shows that the way you supply context can swing answer quality by up to 4.6x. Larger models, it turns out, just lie more convincingly. A small model with RAG can outperform a large model on its own. From those findings the book builds the full Context Engineering picture.
Five context strategies, RAG (the technique that owns 80% of the gain), MCP server design, staged CLAUDE.md design, and Agentic RAG implementation. The next move beyond prompt engineering — grounded in experimental data and 96 production-quality code files.
“Larger models just lie more convincingly. So feed them the truth through context.”
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