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Context Engineering for High-Signal Prompts

2 min read
  • #Context Engineering
  • #Prompt Design
  • #LLMs

Start by framing the user intent

Context engineering is not about packing every scrap of data into the prompt. It's about selecting the right evidence to satisfy the user's job to be done. I start by rewriting the request as:

"The assistant must help the user accomplish X by deciding Y while respecting constraints Z."

When the intent is crisp, the rest of the pipeline falls into place.

Build context ladders

I design "ladders" of context where each rung adds fidelity:

  1. Global policies — governance, tone, legal guardrails.
  2. Session state — what happened in this conversation so far.
  3. Task-specific documents — knowledge bases, code snippets, analytics.
  4. Adaptive retrieval — targeted lookups triggered only when needed.

By making each rung optional, the system avoids sending redundant information when the answer is obvious.

Maintain a context budget

Large context windows are still finite. I run a budgeting function that tracks token usage before building the final prompt. If the payload exceeds the budget, the function prunes low-signal sections first, then compresses with extractive summaries, and finally falls back to retrieval on demand.

const budget = new TokenBudget(16000);budget.add(globalPolicies);budget.add(sessionState);budget.add(taskDocs, { priority: "high" });if (!budget.fits()) {  budget.pruneByPriority();  budget.compressWithSummaries();}

Traceability beats cleverness

Stakeholders need to know why the model answered a question. I tag every snippet with a citation ID and include those IDs in the response. When a human reviews the output, they can trace each sentence to its source. That transparency builds trust faster than any fancy prompt hack.

Iterate with telemetry

  • Log the full prompt, response, cost, and latency for every call (with sensitive data redacted).
  • Cluster low-rated answers to spot systemic gaps in your context sources.
  • Run A/B tests on retrieval strategies to validate improvements before rolling them out.

Treat context engineering as a lifecycle. When you iterate continuously, your assistants stay grounded, helpful, and compliant even as your knowledge base evolves.