Chapter divider plus the opening section: orchestration vs clever wording, dynamic context building blocks, and the retrieval bottleneck (PDF 147–149).
Context Engineering
What is Context Engineering?
Context engineering is rapidly becoming a crucial skill for AI engineers. It's no longer just about clever prompting, it's about the systematic orchestration of context. Here’s the current problem: Most AI agents (or LLM apps) fail not because the models are bad, but because they lack the right context to succeed. For instance, a RAG workflow is typically 80% retrieval and 20% generation. Thus:
● Good retrieval could still work with a weak LLM.
● But bad retrieval can NEVER work even with the best of LLMs.
If your RAG isn't working, most likely, it's a context retrieval issue. In the same way, LLMs aren't mind readers. They can only work with what you give them.
Context engineering involves creating dynamic systems that offer:
● The right information
● The right tools
● In the right format
This ensures the LLM can effectively complete the task. But why was traditional prompt engineering not enough? Prompt engineering primarily focuses on “magic words” with an expectation of getting a better response. But as AI applications grow complex, complete and structured context matters far more than clever phrasing. These are the 4 key components of a context engineering system: Dynamic information flow: Context comes from multiple sources: users, previous interactions, external data, and tool calls. Your system needs to pull it all together intelligently.