@What exactly is context engineering—and how is it different from, or related to, prompt engineering?
In this video, I break it down step by step. We’ll explore how prompts, memory, files, tools, and retrieval-augmented generation (RAG) all fit together to form the “context” that LLMs like ChatGPT use to generate answers.
You’ll learn:
What context engineering actually means.
How it complements prompt engineering instead of competing with it.
Why memory, files, and tools matter for building agentic systems.
A clear framework for thinking about AI conversations.
By the end, you’ll have a complete mental model for how to tune the dials of context and prompts so you can get more consistent, reliable results—whether you’re chatting casually with an AI or designing advanced workflows.
If you found this explainer helpful, let me know in the comments what other AI fundamentals you’d like to see covered.
<a href="https://www.youtube.com/watch?v=p6s82Ax8yrs" target="_blank" rel="noopener nofollow">https://www.youtube.com/watch?v=p6s82Ax8yrs</a>
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Reinier

Context engineering is a framework that encompasses prompts, memory, files, tools, and retrieval-augmented generation (RAG) to optimize how large language models generate responses. Unlike prompt engineering which focuses solely on input text, context engineering considers the entire information ecosystem that influences AI outputs, providing a more comprehensive approach to building reliable agentic systems and improving AI conversation consistency.

Context Engineering, Clearly Explained