Checklist

Applied AI — Retrieval-Augmented Assistant (RAG) Design Checklist

A practical, interactive checklist for designing safe, explainable retrieval-augmented assistants. Guides teams through scoping, data sources, retrieval strategy, prompt templates, human-in-the-loop controls, validation tests (including simple hallucination detection), logging, access controls, and monitoring.

Interactive Tool

Applied AI — RAG Assistant Design Checklist

This interactive checklist helps teams design retrieval-augmented assistants that are useful, auditable, and human-centered. Use it to document decisions, capture risks, create simple hallucination tests, and record owners and next steps. Save a completed checklist as part of your project record or design review.

Date of this checklist review (YYYY-MM-DD)
Person or team responsible for this review
Have you documented the primary user tasks the assistant will support, clear success criteria, and explicit exclusions?
Describe primary users, workflows, data sensitivity, and what the assistant should not do.
What outcomes or KPIs will this assistant influence? E.g., time saved, error reduction, throughput, NPS.
Have you inventoried data sources, assigned owners, and defined freshness/retention rules?
List major source types, update frequency, and owners (e.g., knowledge base, SOPs, product docs, change logs).
Choose the retrieval approach and planned context management.
Planned context size, chunk strategy, and how you will prioritize retrieved docs (recency, relevance, trust score).
Are prompt templates defined to ground responses with retrieved evidence and instruct the model to cite sources?
Paste one or two canonical prompt templates that include evidence citation instructions and fallback behavior.
Will the assistant provide confidence signals and clearly show provenance for retrieved facts?
Numeric threshold or rule that triggers human review (if applicable).
How will you present retrieved items?
Is there a defined escalation or verification path when confidence is low or risk is high?
Describe who reviews, expected SLA, and how corrections feed back into sources or training data.
Are query logs, retrieval evidence, prompts, and final responses recorded for audit and troubleshooting?
Where are logs stored, who can access them, and retention policy?
Are access controls defined (who can query, who can view sensitive evidence, roles for admin vs. user)?
List roles, required approvals, and any dataset-level restrictions.
How will sensitive or personal data be handled?
How will you measure accuracy, drift, and user satisfaction post-deployment? Include metrics and sampling cadence.
Use these starter tests to check for unsupported assertions, invented details, or misattributed citations.
List 2–5 concrete test prompts, the expected correct behavior, and what counts as a failure.
Have you completed deployment items such as staging tests, access provisioning, monitoring hooks, and rollback plan?
Choose planned monitoring cadence.
Rate overall operational risk from 1 (low) to 5 (high).
1.0 10.0
Team or person responsible for ongoing operation and remediation.
Capture immediate follow-ups, owners, and due dates.
Links to architecture docs, source inventories, prompt templates, or audit records.
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