About a year ago I started noticing people in AI circles throwing around a term I hadn't heard before: context engineering. At first I thought it was just another rebranding of prompt engineering — the AI field loves inventing new names for existing things.
But the more I used it, the more I realized it was describing something genuinely different. Something more useful.
The Original Problem with "Prompt Engineering"
When "prompt engineering" first became a thing, it was about wording. How do you phrase a question so the AI gives you a better answer? Add "think step by step." Use XML tags. Give it a role. That kind of thing.
It worked — and it still works for simple interactions. But as people started building real AI products — chatbots that remember things, agents that use tools, systems that search knowledge bases — "prompt engineering" started feeling inadequate.
The problem wasn't just the wording. It was everything that went into the context window.
What Actually Goes Into an LLM's Context
When you send a message to an AI model, the model doesn't just see your message. It sees everything in its context window:
┌──────────────────────────────────────────────┐
│ CONTEXT WINDOW │
│ │
│ System Prompt (instructions, persona, rules) │
│ ─────────────────────────────────────────── │
│ Retrieved Documents (RAG results) │
│ ─────────────────────────────────────────── │
│ Conversation History (prior turns) │
│ ─────────────────────────────────────────── │
│ Tool Results (API calls, search results) │
│ ─────────────────────────────────────────── │
│ Current User Message │
└──────────────────────────────────────────────┘
Every one of those components is a design decision. How long is the system prompt? How much conversation history do you keep? How many search results do you inject? How do you format tool outputs so the model actually uses them?
That's what context engineering is — making all those decisions deliberately instead of accidentally.
Why It Matters More Than Prompt Wording
Here's a scenario I see constantly: someone builds an AI assistant. The prompts are beautifully written. But in production, users have long conversations, the context fills up, and the model starts forgetting earlier instructions or hallucinating.
The problem isn't the prompt wording. It's that nobody thought about conversation history management. Nobody decided what to do when the context window gets full. Nobody designed the context — they just wrote a prompt and hoped for the best.
Context engineering fixes this by making you think about:
1. What goes in the system prompt vs. what gets injected dynamically
A common mistake is cramming everything into the system prompt — instructions, examples, reference data, user details. But some of that information should be injected per-request, because it changes and doesn't need to be in every single API call.
2. How to handle conversation history
Full conversation history is fine for short sessions. But after 20 turns, you're burning tokens on conversation history that the model doesn't actually need. Smart systems summarize old turns and keep only recent ones verbatim.
3. Where you position critical information
Research shows LLMs pay more attention to content at the beginning and end of long contexts — content buried in the middle gets less attention. If you have critical instructions, put them at the top of the system prompt. If you have critical retrieved documents, put them nearest to the user message.
4. How much retrieved content is actually helpful
More isn't always better. Retrieving 20 documents when the model only needs 2 adds noise that degrades output quality. Getting retrieval right — not just "more context" but relevant context — is a core context engineering skill.
A Simple Example
Imagine you're building a customer support bot for a SaaS company. The naive approach:
System prompt: [Everything — company info, all policies, tone guidelines,
escalation rules, product FAQs, pricing details — 4,000 tokens]
The context-engineered approach:
System prompt: [Core instructions only — role, tone, key behaviors — 400 tokens]
Dynamically injected per-request:
- User's subscription tier and account status
- Relevant FAQ chunks retrieved based on their specific question
- Their recent support history (last 2 tickets)
The second version uses context window space more efficiently, keeps information current (account status retrieved live), and gives the model exactly what it needs for this user's this question — not a dump of everything.
Context Engineering for Agents
If you're building AI agents, context engineering becomes even more important.
Agents run in loops — they think, take an action, receive a result, think again. Every loop adds more content to the context. Without careful management, the context grows unbounded and the model loses track of its original goal.
Good agent context engineering includes:
- A compact "current state" summary updated each step instead of the full action history
- Limiting tool result size (truncate or summarize long outputs)
- A clear "working memory" section that tracks what the agent has accomplished
- Checkpointing — saving state to memory when context gets long and starting fresh
This is why the term "context engineering" feels particularly apt for the agent era. Agents don't just receive a context once — they actively modify it over time.
Getting Better at Context Engineering
The main shift is from thinking about "what do I say in my prompt?" to "what information does the model need, when does it need it, and where in the context should it live?"
Practical ways to level up:
-
Log your full contexts — Print the entire prompt (including all injected content) and actually read what the model sees. Most people have never done this.
-
Measure token usage — Know how large each component is. If your system prompt is 3,000 tokens and you have a 4,096-token limit, something needs to change.
-
Test context degradation — What happens to your application after 50 turns? After 100? Long-running sessions surface context management problems quickly.
-
Position critical information intentionally — Move your most important instructions and data to the beginning or end of the context, away from the middle where attention fades.
If you want to dive deeper into context engineering for agents specifically, the Agents track on MasterPrompting.net covers context engineering as a dedicated lesson with practical examples.
The Bottom Line
"Context engineering" isn't just a new word for prompt engineering. It's a more accurate description of what actually matters when you're building production AI systems.
The prompt is one piece of the puzzle. The full context — everything the model sees when it generates a response — is the whole puzzle.
Start treating it that way and your AI systems will be more reliable, more efficient, and easier to debug.
