A concrete example
Before answering, the agent pulls the project rules, the useful files, and examples of the output you expect.
Why it matters
Good context cuts down errors, boosts consistency, and makes agents more predictable.
You'll see it in agents, RAG, instruction files, and workflows that pick the context before answering.
Don't mix it up with
System prompt: The system prompt is the top-level instruction that frames how the model behaves during the conversation.
RAG: RAG lets an AI answer using documents pulled up at the moment you ask the question.
Common mistakes
- Cramming everything into the prompt instead of being selective.
- Giving contradictory rules.
- Forgetting to remove information that's no longer true.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: Before answering, the agent pulls the project rules, the useful files, and examples of the output you expect.
- I keep the main trap in mind: Cramming everything into the prompt instead of being selective.
Quick questions
What is Context engineering in AI?
Context engineering is about giving the model the right documents, rules, examples, and tools at the right time.
Where will I run into Context engineering?
You'll see it in agents, RAG, instruction files, and workflows that pick the context before answering.
Which word should I read next?
Start with System prompt, RAG, AGENTS.md.