Context Construction Rules: Selection, Budgets, and Provenance
Explore how to define and enforce deterministic context construction rules that convert retrieved evidence into a bounded, auditable prompt context. Understand selection criteria, token budgeting strategies, and citation standards to build secure and compliant AI systems with transparent provenance and reproducible outputs.
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Choosing simple retrieval over hybrid, reranking, or routing decides what evidence gets found. It says nothing about which of that evidence the model actually sees, or in what order. Retrieval finds candidate evidence. Context construction decides which of that evidence the model actually sees and in what structured order. This boundary layer is a deterministic policy that turns a set of retrieved candidates into a bounded, attributable prompt context with known constraints on size, sensitivity, and traceability. Most quality failures cluster right here: a strong retrieval set can still produce an ungrounded answer when duplicates crowd out key passages, when the instruction hierarchy is ambiguous, or when provenance doesn’t survive packing.
Specify context construction as testable rules. Given the same candidate set, the same metadata, and the same policy version, the output should be reproducible: a final packed context, plus a machine-readable record of what was included, what was excluded, and why. That keeps our copilot aligned to quality and latency constraints without drifting into prompt tactics.
The following visual shows the end-to-end packing pipeline from retrieved chunks through hygiene and budgeting to a final attributable context.
