How to engineer context for enterprise AI apps: a step-by-step guide

Context engineering is the discipline of deciding what the model reads before it writes. A good stack is small, current, and relevant. Here's how to build one.

Step 1 — Define the job to be done

Write the task in one sentence: who is asking, what decision depends on the answer, and what counts as "good." If you can't name the decision, you can't choose the context.

Step 2 — Inventory the sources

List every source the AI might need: policy docs, CRM records, product specs, recent chat history, user preferences. Mark each one as always needed, sometimes needed, or rarely needed.

Step 3 — Add retrieval, not dumping

Don't paste whole manuals. Use retrieval (search + embeddings) to pull only the passages that match the current question. See hybrid search for RAG for the mechanics.

Step 4 — Order the stack

Put system rules first, user instructions next, retrieved evidence after that, and the prompt last. Models weight recent tokens more heavily; the question goes near the bottom for a reason.

Step 5 — Enforce grounding and citations

Require the model to answer only from retrieved context, and to cite the source. Read grounding and honest refusal and citations in enterprise AI for patterns that work.

Step 6 — Measure and trim

Track answer quality, latency, and token cost. If the window is 80% full, you're probably carrying stale context. Cut, then re-measure.

For help designing this across workflows, see the Silverberry AI Platform or our AI Consulting Services.