Make the prompt inspectable

Structuring Context

Structure tells the model what each piece of context is for. Without labels, order, and priority, even good evidence can become a noisy pile.

Key facts

Structuring Context

  • Context structure gives each token a role: task, priority rule, evidence, conflict, example, memory, or output contract.
  • Source cards make provenance, freshness, authority, and retrieval reason visible before generation starts.
  • Decisive rules and evidence should not be buried in long middle sections of a prompt.
  • The output contract is part of context because it defines how the answer will be judged and reused.

Context structure is an interface. The model is reading it, but engineers and reviewers must be able to inspect it too. A well-structured prompt exposes priority, provenance, conflicts, and expected output. A poorly structured prompt asks the model to infer all of that from a long concatenation of text.

"current language models do not robustly make use of information in long input contexts"
Lost in the Middle: How Language Models Use Long Contexts

Order by control, then evidence, then examples

The first screen of context should answer: what is the job, what rules govern it, and what counts as success? Then provide evidence in a form that distinguishes source authority and recency. Put examples after rules and evidence unless the example is itself the task. Examples are powerful, but they can accidentally teach the model to copy an old pattern over a current instruction.

"Lost in the Middle" is often cited as an ordering warning because models can use information differently based on position. Do not bury the decisive rule between low-signal passages. If a fact decides the answer, make it visible near the task, in a summary, or in a priority section.

Use source cards, not anonymous snippets

A source card is a compact wrapper around a passage. It should include title, source type, owner or publisher, date, retrieval reason, confidence, and canonical URL or identifier. This small amount of metadata prevents the model from treating a current policy, a stale draft, and a user complaint as equal evidence.

Source cards also make debugging easier. If the answer is wrong, you can ask whether the retriever found the wrong card, the reranker promoted the wrong card, the assembler placed it poorly, or the generator ignored it.

Separate instruction context from evidence context

Instructions say how to behave. Evidence says what is true for this task. Mixing them creates priority bugs. A customer quote should not override a policy. A retrieved blog post should not change the required output format. A memory note should not outrank an explicit instruction in the current request.

A practical prompt can use visible sections:

  1. TASK: the current user goal and success criteria.
  2. PRIORITY RULES: constraints, policies, and decision order.
  3. EVIDENCE: source cards with quoted or extracted passages.
  4. CONFLICTS: known disagreements between sources.
  5. OUTPUT CONTRACT: required fields, tone, citations, and refusal boundaries.

Delimiters help only when sections have roles

Delimiters are not magic. XML tags, Markdown headings, and code fences help because they make boundaries clear, not because a specific punctuation pattern is inherently superior. Choose a format that your team can generate, inspect, and test. If context is assembled by software, stable structured fields often beat hand-written prose.

Make conflicts explicit

Many prompts fail because they hide disagreement. If two sources conflict, do not hope the model notices. Add a conflict note: which source says what, which is newer, which is more authoritative, and what uncertainty remains. This turns contradiction from a generation trap into an answer requirement.

Self-RAG is useful as a conceptual reminder: retrieval does not have to be a fixed dump of passages. Systems can retrieve on demand, critique evidence, and decide whether more context is needed. Even without training a Self-RAG model, application prompts can ask for evidence sufficiency checks before final answers.

Write an output contract

The output contract is part of context. It tells the model how the answer will be judged: fields, citation requirements, uncertainty language, prohibited claims, and next actions. For a research answer, require claim-level citations. For a coding agent, require changed files and verification commands. For a support bot, require the policy source and escalation boundary.

A good output contract reduces post-processing and makes evaluation concrete. If the answer must cite the source card IDs it used, you can measure whether the model grounded its claims in provided evidence.

Sources Used

Cite This Page

Curated Context. "Structuring Context." Accessed July 6, 2026. https://curatedcontext.com/structuring

https://curatedcontext.com/structuring