Definitions
R.A.G. stands for Retrieval-Augmented Generation. It’s a way to make AI models (like ChatGPT) smarter by programming them to“look up” extra information before answering your question.
<Imagine you’re taking a test, but you get to flip through your notes before you answer>
Context Engineering refers to designing and structuring the information, prompts, and surrounding details that an AI system uses to understand and respond accurately.
<Imagine you’re in theatre production and your actors are AIs - you want to get the best out of their performances so you tweak the set, lighting, and background to guide the story in the right direction>
Why is RAG useful?
It helps AI models answer questions about stuff they weren’t directly trained on.
It’s particularly good at things like customer support and document search.
RAG systems usually work by searching for chunks of info, then feeding those chunks into the AI’s “context window” (basically, RAG is RAM for that answer).
So what’s the deal with context engineering? - a new buzzword
If you just dump a ton of stuff in RAG, the model can get confused, miss important details, or even ignore what matters most.
Have you ever written a one-page (font size: minuscule) “cheat-sheet” for an exam only to be left unable to read it during the test?
The more you cram in, the less useful the cheat sheet AI’s answers get.
So the real secret sauce isn’t just about retrieving info (via RAG), but about engineering exactly what goes into that context window.
In other words, if you want to encourage maximum success it is more helpful to pick out and hand-over carefully curated information to your friend rather than a random pile of notes.
Context Engineering basics
(I am severely unqualified to proffer more)
Be picky about what info you feed the AI.
Use smart search, re-rank and filter data to facilitate only “the best” makes it through
Regularly test and tweak data you put in, so the AI’s answers stay sharp
In Summary
RAG moved the field forward. and the next level is context engineering.
Curating ...
structuring ...
... optimizing what the AI sees ...
... so it can actually use it well.
RAG = giving the AI access to more info.
Context engineering = right info, in the right format, at the right time.
If yes to either questions then ——>
or Buy this jumper 👇🏽
Share this post