Context engineering is what makes AI magical

Everyone’s busy tweaking prompts, swapping models, chaining tools. Yeah, models are getting better. Tools are getting fancier. But none of that matters if your context sucks.

Here’s the uncomfortable truth: the main thing that really matters when building AI agents is the quality of the context you give the model. Models are so good that most SOTA models can do most things with outstanding quality.

Two products could be doing the exact same thing, but one feels magical and the other feels like a cheap demo. The difference? Context.

Same model, same task, different outcome

What is context?

Context is everything the model sees before it produces tokens. It includes:

the system prompt (what you tell the model it is)
the user message (what the user is asking)
any external information, tools, or retrieved documents you stuff in before the call
implicit context like who the user is, what they’ve done before, what they want right now, etc.

The better this context, the better the model performs. Garbage in, garbage out, still true in 2025.

Context > Model

Models are already better than most of us at most tasks. But most AI tools still underperform, not because the model is bad, but because we’re feeding it a half-baked view of the world.

Let’s look at RAG as an example.

From Naive RAG to Agentic RAG

Naive RAG just dumps the top 3 chunks into the prompt and hopes for the best. Useful, sometimes. But the moment you move beyond toy examples, this starts to fall apart.

Agentic RAG, builds a contextual snapshot that includes data from multiple sources:

the question
related documents
source structure
metadata
and critically, the user’s intent and environment

For example, a coding agent shouldn’t just embed source files and search them. It should know:

how to find and read any file in your repository
which files were changed recently
which files are open in your IDE
what the LSP says about types and errors
even what production logs and metrics say

That’s context. And that’s the difference between “meh” and “wow”.

Email example: the magic of good context

Let’s say you’re a CTO at a startup. You get an email:

Hey Boris, just checking if you’re around for a quick sync tomorrow. Would love to chat through a few ideas from our last call.

A decent AI email tool will reply like:

Dear Jim,

Thank you for your message. Tomorrow works for me. May I ask what time you had in mind?

Best regards, Boris

Who writes emails like this?

A magical agent will do a few things first:

check your calendar: you’re in back-to-back calls all day.
look at previous emails to this person: you’re friendly, informal.
scan recent meeting notes: this person is pitching a joint partnership.
pull in your contact list: they’re a senior PM at a partner org.
apply your system prompt customisation: “be concise, decisive, warm”

And finally generate and autonomously send an email that actually is helpful:

Hey Jim! Tomorrow’s packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if works.

That’s magic. Not because the model is smarter, but because the context is richer.

Building context before generating tokens

Build context like it’s your product

You wouldn’t build a product without thinking deeply about state, user intent, and interaction history. So why treat your AI agent like a stateless chatbot?

Context engineering is the new prompt engineering.

It means:

Designing the right structure and format for context
Knowing what context actually helps the model perform
Building pipelines to fetch, transform and deliver this context at runtime
Constantly improving context quality with feedback loops

TL;DR

Models are great.
But context is king.
Context is the difference between a dumb assistant and a superpowered teammate.
Build your agents like you build your products: obsess over what they know, when they know it, and how they use it.

If your agent isn’t magical yet, don’t swap the model, fix your context.