Template

AI‑Assisted Knowledge Agent — Spec Template

An interactive specification template to design, govern, and operate retrieval‑augmented knowledge agents. Collects purpose, ownership, data sources, retrieval rules, prompt designs, safety and escalation rules, logging and monitoring requirements, SLAs, and an evaluation plan so teams can build explainable, auditable, and maintainable assistants.

Interactive Tool

AI‑Assisted Knowledge Agent — Spec Template

This interactive spec helps teams design retrieval‑augmented assistants that are explainable, auditable, and safe. Use it to capture the agent's purpose, allowed data, retrieval and ranking rules, prompt designs, fallback behaviour, monitoring needs, ownership and maintenance plans. Fill required fields, add examples, and save a copy so the agent can be reviewed, tested, and governed consistently across projects.

Guidance: be concrete about data sources and freshness windows, state who owns the agent and who reviews outputs, and include a simple evaluation plan with sample test queries and acceptance criteria.

A short name for this assistant spec (e.g., 'Support KB Assistant — Tier 1').
Version identifier for change control (e.g., 2026-09-01, v1.0).
Describe the agent's primary use cases, limits, target users, and what the agent must NOT do. Be specific about decisions it supports versus decisions requiring human approval.
List the primary owner (person/team), reviewers, escalation contacts, and business stakeholders. Include roles responsible for data, model, and production operations.
Describe the assistant's persona, style, and constraints (e.g., professional, concise, cite sources, avoid speculation). Provide a short system message example if useful.
List each allowed content source (document collections, databases, APIs, internal KBs) and include URIs, storage names, or dataset identifiers. Only these sources may be used for retrieval.
For each allowed source, specify expected freshness or sync cadence (e.g., daily, real-time, 90 days). Note sources that are static versus frequently updated.
Choose the highest sensitivity level present in allowed sources and add details in 'security_controls'.
Describe access controls, RBAC, encryption, audit logging, data residency constraints, and any compliance frameworks that apply (e.g., HIPAA, GDPR). Include who approves access.
Specify vector store(s), embedding model(s), chunking rules, similarity metric, reranking rules, hybrid retrieval logic (BM25 + semantic), maximum retrieved passages, and recency weighting if any. Be explicit about deterministic vs. probabilistic steps.
Provide the canonical system message, user prompt template, temperature / decoding parameters, few-shot examples or instruction templates, and rules about how retrieved context is incorporated (e.g., 'only use top-3 passages and always quote sources').
Specify how the assistant will attribute content (inline citations, numbered references, links), and how the source of each claim is recorded and surfaced to users.
Define when the assistant should decline to answer, ask for clarification, or escalate to human support. Include thresholds such as low confidence, contradictory sources, or sensitive topics. Provide escalation contacts and SLAs.
List what is logged (queries, retrieved passages with IDs, prompts, model responses, confidence scores, user interactions), log retention policy, and who can access logs. Note privacy-preserving measures (redaction, hashing).
Define SLA targets for latency, uptime, and error rates relevant to the agent's use (e.g., 95th percentile latency < 1.5s for responses).
Select KPIs to track and provide thresholds or targets in the next field.
For each selected KPI, define target values, how they are measured (sample size, ground truth process), reporting cadence, and alert thresholds.
Provide sample test queries, expected correct responses or acceptance criteria, who performs evaluations, how often, and pass/fail thresholds for rollout. Include adversarial tests for hallucination and bias.
State the cadence and ownership for data refresh, reindexing, prompt or model tuning, and periodic audits. Include rollback and versioning approach.
Describe how to respond to incidents (misinformation, data leaks, outages), notifications, and postmortem responsibilities.
Staged rollout steps, pilot groups, rollback criteria, and approval gates for changes to prompts, sources, or models.
Include 2–5 example queries with ideal assistant responses and source attributions. These are used for testing and training reviewers.
List potential failure modes (bias, stale data, IP leakage), likelihood and impact, and mitigation strategies.
List links to related design docs, datasets, dashboards, audit logs, or compliance paperwork.
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