Context Engineering: Beyond Prompt Writing
By Karthick Selvam · Published · Updated
Context engineering is the practice of deciding what information goes into a model's context window, and how, for each step of a task.
Why it matters
Model output depends heavily on what it can see. Missing, outdated or noisy context causes most failures in LLM applications.
What it involves
Selecting relevant documents through retrieval, summarising long histories, structuring tool results, ordering information sensibly and keeping instructions consistent.
Relation to prompt engineering
Prompt engineering focuses on wording instructions. Context engineering covers the full information pipeline feeding the model, especially in agents and multi-step workflows.
Skills required
Retrieval design, data cleaning, chunking strategies, evaluation and an understanding of model context limits.
Frequently asked questions
Is a bigger context window a substitute?
Not fully. Long contexts help, but irrelevant information can still degrade answers and increases cost.
Related programmes
- Agentic AI with LangChain & LangGraph
- Building AI Agents with LangGraph & CrewAI
- Multi-Agent Systems: Design & Orchestration
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