Context Engineering

What It Is

Context engineering is the practice of shaping the information a model sees so it can answer accurately, safely, and in the right voice. The context window is a working set, not an archive. You are choosing what the model should pay attention to now.

Core Constraints

Modern models are strong, but the context window is finite and expensive. Every token you add competes for attention, increases latency, and raises the odds of pulling in irrelevant or unsafe material. Order matters too: recency bias and instruction hierarchy can change outcomes.

Building Blocks

• System and developer instructions that define intent and boundaries.
• Task spec with acceptance criteria and examples.
• Evidence snippets from retrieval or citations.
• Tool outputs that must be grounded and verified.
• Memory summaries to preserve long running state without bloat.

Pattern: Intent, Evidence, Plan, Action

Start with intent and constraints, then provide evidence, then a brief plan, then the action. This mirrors how people and models reason and keeps the response anchored in source material.

Pattern: Summarize and Compress

If a conversation or document grows long, summarize into a short, durable note and replace raw history. Compression beats truncation because it preserves the semantics you care about and frees tokens for new evidence.

Pattern: Guard the Boundaries

Treat retrieved content as untrusted input. Clearly separate instructions from evidence, redact secrets, and avoid letting documents override system or developer rules. This prevents prompt injection and keeps model behavior stable.

Practical Checklist

• Define the minimum viable context for each task.
• Use structured sections with clear headings and ordering.
• Prefer short, high signal excerpts over long dumps.
• Track and refresh summaries as facts evolve.
• Log what the model saw when debugging failures.