Retrieval‑Augmented Agent (RAA) Spec Template

Purpose

This spec template helps teams ship retrieval‑augmented agents (RAAs) that are explainable, auditable, and safe. Use it to record design decisions, operational constraints, test cases, and sign‑off criteria so answers from the agent are traceable and trustworthy.

How to use this template

Fill each section with concrete, testable choices. Prefer specific names, thresholds, example queries, and owner names over vague statements. Keep this spec with the RAA's repository, deployment playbook, and audit logs.

1. Scope & Use‑Cases

Describe the RAA's intended users, primary jobs-to-be-done, and operational boundaries. Include explicit non-goals.

• Primary users (roles): e.g., field technicians, HR reps, customer support agents.
• Primary use-cases: e.g., onboarding Q&A, troubleshooting steps, policy lookups.
• Non-goals / prohibited uses: e.g., legal advice, prescribing treatment, financial approvals.
• Operating hours and availability expectations.

2. Canonical Sources (Source Inventory)

List authoritative sources the agent may retrieve from and the source of truth for each domain of knowledge. For each source include ownership, update cadence, access controls, expected freshness, and indexing notes.

• Document repositories (e.g., Confluence spaces, internal SharePoint URLs)
• Product manuals and tagged knowledge bases
• Databases, structured records, or canonical spreadsheets
• Third‑party vendor docs (with license/usage rules)

3. Chunking Rules & Metadata

Specify how raw content is split into retrieval units (chunks) and what metadata is stored with each chunk.

• Chunk size target (tokens / sentences / characters) and allowed overlap
• Chunk boundaries rules (paragraph boundaries, section headers, table handling)
• Required metadata fields: source_id, doc_title, section_heading, last_updated, author, confidence_tag, access_tier
• Versioning: how updates to source documents re‑chunk and reindex

4. Retrieval Method & Ranking

Describe retrieval pipeline and ranking logic.

• Embedding model(s) used, version, and embedding pipeline
• Vector store and approximate nearest neighbor config
• Hybrid retrieval rules (keyword filters + vector similarity)
• Ranking signals and weights (semantic score, recency, source trust score, access_tier match)
• Top‑k default and max limits for candidates

5. Retrieval Scoring & Thresholds

Define numeric thresholds and calibration procedures so retrieval quality is measurable and adjustable.

• Similarity score thresholds for accepting a retrieval (example: cosine > 0.78)
• Fallback thresholds that trigger human review or explicit disclosure that the answer is uncertain
• Calibration plan: periodic reruns against gold queries to adjust thresholds

6. Citation & Provenance Format

Specify how retrieved evidence is shown to users and how provenance is recorded for audits.

• In‑response citation format (e.g., [DocTitle §Section | source_id | last_updated])
• Show snippets with highlighted source text and a persistent link to the original
• Log format for provenance: request_id, user_id, timestamp, query, returned_chunk_ids, similarity_scores, model_version
• If content is synthesized across sources, present an evidence list and explicit statement of uncertainty

7. Prompting & Response Policy

Capture system prompts, safety guardrails, and user-facing templates.

• System message(s) with role, tone, and instruction constraints
• Response length limits and structure (summary, citations, next steps, disclaimer)
• How to state uncertainty (e.g., "I may not have full information; verify with X")
• Escalation prompts that request human review for high‑risk answers

8. Fallback Behavior & Failure Modes

Define what happens when retrieval fails, the model hallucinates, or sensitive content is encountered.

• Responses on low confidence: explicit disclosure + suggested human contact
• If no relevant documents found: refuse to answer and offer ways to refine the question
• Behavior when conflicting sources are retrieved: present conflicts with provenance
• Failure to access a required datastore: return a standard error and create an incident ticket

9. Sensitive Data Rules & Access Control

List policies governing PII, PHI, secrets, legal, and restricted data.

• Access tiers and role-based retrieval permissions
• Redaction rules for transcripts, logs, and returned snippets
• Prohibited content types and safe‑handling steps
• Data retention and audit requirements

10. Rate Limits, Quotas & Cost Controls

Specify per-user, per-department, and system-wide limits and expected cost model.

11. Test Cases & Acceptance Criteria

Provide concrete test queries, expected citations, and pass/fail rules.

• Functional tests: sample queries where correct answer and supporting chunks are listed
• Adversarial tests: ambiguous questions, missing context, attempts to extract secrets
• Regression tests: previously solved tickets and knowledge checks
• Acceptance criteria: e.g., precision@k > 0.8 on gold set; no PII leakage in 1000 adversarial trials

12. Monitoring Signals & KPIs

Define what to monitor and alarm on for safe operation.

• Signal examples: query volume, avg retrieval score, citation coverage, escalation rate, user feedback score, hallucination incidents
• Dashboards and refresh cadence
• Alert thresholds and on‑call routing

13. Rollback Plan & Incident Response

Describe steps to take when the RAA must be disabled or rolled back.

• Immediate mitigation (disable model inference, switch to read‑only mode)
• Communication templates for affected users
• Post‑mortem owner and timeline

14. Ownership, Governance & Sign‑Off Checklist

List the individuals and approvals required before launch.

• Product owner
• Data owner(s) for each canonical source
• Security/privacy reviewer
• Compliance/legal reviewer (if applicable)
• DevOps / SRE contact
• Sign‑off items: test pass results, monitoring configured, rollback tested, documentation published

15. Example Spec Snippet

Provide a small, concrete example that demonstrates expected answers and citations.

Query: "How do I replace the air filter in Model X-2000?"

Expected behavior: Return a concise step list, cite "Maintenance Manual v3.2 §Air Filter Replacement" with link, include estimated time, and show a "Contact Support" escalation if the user reports damage.

16. Implementation Notes & Next Steps

Recommend periodic review cycles, a process for onboarding new sources, and training for human reviewers. Consider converting this template into an interactive onboarding form (collecting source URLs, owners, thresholds) so each deployed RAA has a registered spec that can be queried programmatically.

Sign‑Off Checklist (Quick)

1. All canonical sources listed and owners assigned
2. Chunking and embedding pipeline defined and tested
3. Retrieval thresholds set and calibration tests passed
4. Citation/provenance format implemented and logged
5. Fallback and escalation behaviors implemented
6. Sensitive data rules validated and enforced
7. Monitoring & alerts configured; KPIs baseline recorded
8. Rollback procedure tested
9. Stakeholders signed off