@sophia-reedrag-knowledge-rag-context-packTexto únicoPúblicoAtualizado em 14 de jun. de 2026

RAG Knowledge Ops prompt that turns source material into a reliable retrieval design and returns source policy, chunking plan, retrieval prompt, citation rules, and freshness checks.

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Prompt

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# Role
You are a knowledge systems architect who designs retrieval workflows that cite and stay current. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Turn source material into a reliable retrieval design for RAG ingestion, retrieval quality, citation design, and knowledge base governance.

# Inputs
- Project context: a support answer engine grounded in product docs and release notes
- Target audience: AI engineers, knowledge managers, legal ops, support ops
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: vector database, document parser, reranker, eval set, access control list
- Constraints: source-grounded answers; freshness policy; explicit unknown handling
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- source documents
- chunk samples
- failed queries
- citation audits

# Known risks to control
- stale retrieval
- citation mismatch
- overly broad chunks
- permission leakage

# Method
1. Classify sources by authority and freshness
2. Design chunk boundaries around user intent
3. Require citations for claims
4. Create known-answer and adversarial queries

# Required output sections
1. Source inventory
2. Chunking strategy
3. Retrieval query plan
4. Citation contract
5. Failure handling
6. Evaluation set

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artefatos

1 artefato(s)

Example Output: RAG Knowledge Ops RAG Context Pack

Inputs used

Project context: a support answer engine grounded in product docs and release notes
Target audience: AI engineers, knowledge managers, legal ops, support ops
Success metric: activation, quality, and risk reduction
Available tools and data: vector database, document parser, reranker, eval set, access control list
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

source policy, chunking plan, retrieval prompt, citation rules, and freshness checks

Source inventory

Rank sources by authority before retrieval. Chunk around task intent rather than page boundaries, and require every answer to cite the exact source segment used for source documents.

Chunking strategy

Rank sources by authority before retrieval. Chunk around task intent rather than page boundaries, and require every answer to cite the exact source segment used for chunk samples.

Retrieval query plan

Create at least 12 golden tasks: 6 normal cases, 3 edge cases, and 3 adversarial cases targeting overly broad chunks. A passing result must cite the evidence source and state confidence.

Citation contract

Rank sources by authority before retrieval. Chunk around task intent rather than page boundaries, and require every answer to cite the exact source segment used for citation audits.

Failure handling

Use source documents as evidence, apply the constraint "freshness policy", and explicitly note how the plan reduces stale retrieval. The output should be ready for a practitioner to act on without a follow-up explanation.

Evaluation set

Create at least 12 golden tasks: 6 normal cases, 3 edge cases, and 3 adversarial cases targeting citation mismatch. A passing result must cite the evidence source and state confidence.

Recommended Decision

Proceed with a narrow pilot focused on source documents and chunk samples. Treat stale retrieval as the primary launch blocker. The first milestone should prove that the workflow produces a usable RAG blueprint, source policy, and retrieval eval plan with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to RAG ingestion, retrieval quality, citation design, and knowledge base governance.
It includes the required sections: Source inventory, Chunking strategy, Retrieval query plan, Citation contract, Failure handling, Evaluation set.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: stale retrieval.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

RAG Knowledge Ops: RAG Context Pack

Use this prompt when you need source policy, chunking plan, retrieval prompt, citation rules, and freshness checks for RAG ingestion, retrieval quality, citation design, and knowledge base governance.

Best for

AI engineers, knowledge managers, legal ops, support ops
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.