29 December 2025
Context Engineering 101 - And Why You Should Care
The first article in the Context Engineering series. An introduction to what context engineering is, how we got here, and why 2026 is the year it becomes essential.
Let me take you back to late 2022 when ChatGPT dropped and everyone was scrambling to figure out how to talk to this thing. The only way in was through the prompt box, so naturally prompt engineering became the skill to learn.
That wave carried through most of 2023 until RAG came along with a smarter idea: instead of stuffing everything into prompts or fine-tuning models, you could pull in relevant information on the fly.
By 2024 the ask had changed. We didn't just want AI to answer questions, we wanted it to do things on its own. Agents took center stage with frameworks like LangChain, LangGraph, AutoGen, CrewAI, and Karpathy said it clearly: this is going to be the decade of agents.
Then late 2024, Anthropic quietly released MCP (Model Context Protocol), a way to give agents more context. Most people ignored it at first, but by February 2025 it exploded. Everyone from food delivery apps to JP Morgan was adopting it, and soon there were more MCPs than people actually using them.
But somewhere in this rush, people missed the obvious pattern. Prompts, RAG, agents, MCP, all of it was really about one thing: getting the right context to the model. We were doing context engineering the whole time, we just never called it that.
And now we're hitting walls. I've been there myself, trying to generate large structured JSONs while keeping multiple such outputs in context, along with company-specific instructions in the system prompt. You watch your 200k context window fill up and realize the problem isn't the model, it's everything you're feeding it.
When you keep adding to context without managing it, you get bloating. And bloated context doesn't just slow things down, it starts poisoning your outputs with noise you can't trace. Your agents fail not because the model is weak, but because the context is a mess.
As Karpathy put it:
"The delicate art and science of filling the context window with just the right information for the next step."
Till now we were focused on building and adding context. 2026 is about learning to manage it. And if you're building anything serious with agents, this isn't optional anymore.
Context engineering is at the core of what we build at Chromatic Labs. It's how we maintain brand consistency, preserve scene coherence across frames, and generate videos that actually feel intentional — not random. When your AI remembers your brand guidelines, your product details, and your creative direction across every generation, that's context engineering at work.
Next in the series: Context Engineering 102, where we break down a practical framework for actually doing this.
