How to use this checklist

RAG is a system, not a feature flag. These items are ordered so that data and governance decisions happen before you pour effort into prompt polish. Walk through each section with a single accountable owner from IT or data, a product sponsor, and someone who can speak to compliance. Check items off only when evidence exists—sample queries, logs, or signed policies—not when a ticket is open.

If you are synchronizing with business stakeholders, translate technical checkpoints into outcomes: “We can prove which document supported each answer,” “Role X never sees branch Y files,” and “We can roll back yesterday’s index if quality regresses.” That language keeps reviews short.

Version the checklist itself. At milestone reviews, append dated notes: which risks closed, which new constraints appeared (for example, a ban on certain cloud regions), and which items slip to the next phase with explicit acceptance of residual risk. That discipline prevents silent debt accumulation.

Data readiness—deeper notes

Start by inventorying sources of truth: wikis, PDFs, tickets, databases, and SaaS exports. For each, record refresh latency, legal classification, and who can grant API or export access. Bad RAG often traces to mixing marketing PDFs with engineering specs without labeling; users cannot tell which is authoritative.

Define freshness SLAs explicitly: some corpora are nightly batch, others are near-real-time via CDC. The ingestion pipeline should tag document versions so answers do not blend superseded policies with current ones. Where OCR or HTML-to-text is lossy, budget cleanup time or accept that those sources need human QA gates.

Duplicates and near-duplicate policy memos confuse ranking; deduplicate or mark precedence so retrievers do not surface conflicting guidance with equal scores. When multiple departments publish overlapping content, establish a canonical owner field in metadata before embeddings go live.

Retrieval quality—beyond benchmarks

Chunking is not universal: structured data may want row-level retrieval; long policies may need section-aware splitting. Run side-by-side tests on real user questions, not toy sentences. Hybrid search—lexical plus vector—often rescues SKU codes, legal citations, and jargon that pure embedding search misses.

Rerankers add latency but improve precision when the top-k from first-stage retrieval is “almost right.” Log failed queries: empty results, low scores, or retrieved chunks that models ignore. Those logs become your roadmap for metadata enrichment (tags for product line, region, effective date) far faster than guessing new prompts.

Budget time for “retrieval UX”: highlighting spans, deep-linking to page anchors, and clarifying when two chunks disagree. Models can only work with what retrieval serves; presenting context clearly reduces unnecessary regeneration loops and saves tokens.

Security, governance, and compliance

Map identity from your IdP through to the vector index: group membership, labels, or ABAC attributes should filter candidates before the LLM sees text. Audit logs should bind user, query, retrieved IDs, and model version—not just chat transcripts. Define retention: how long you store queries, embeddings, and raw text mirrors.

Work with legal early on cross-border data flow, model provider terms, and whether fine-tuning or automated training on tickets is allowed. The checklist items above surface surprises in week two instead of at internal audit.

Define incident response for AI-specific failures: what to do when a bad document pushes toxic answers, when embeddings corrupt, or when a partner model has an outage. Run a tabletop exercise before launch so on-call runbooks are not written during the first live incident.

Model, prompting, and answers users trust

Pick baseline models for reasoning quality, latency, and cost—but invest in prompt patterns that force citation and refuse when context is thin. Add structured output where downstream systems consume answers. Test temperature and max tokens against hallucination rate on your domain.

Expose confidence heuristics to product design: when to show sources, when to ask a clarifying question, and when to escalate to a human. Users forgive slower answers more than wrong ones with no traceability.

Align answer formats with downstream systems early: if customer service tools expect HTML but your pipeline emits Markdown, you pay for adapters twice. The same applies to citation formats—consistent chunk IDs make analytics and human audit far cheaper.

Production rollout, monitoring, and iteration

Pilot with a bounded audience and success metrics: deflection rate, time-to-resolution, or researcher hours saved—whatever matches the workflow. Monitor p95 latency, error rates on embed and retrieve calls, drift in feedback thumbs-down, and cost per successful task.

Ship a defined fallback path when retrieval is empty or the model abstains. Tie product backlog to failure clusters: if “policy updates” drive errors, fix ingestion before tweaking prompts. For architecture choices between retrieval approaches, see RAG vs fine-tuning; for agent-centric workflows layered on RAG, see AI agent development.

Document “definition of done” for go-live: which metrics in green for one week straight, which failure screens are acceptable, and who can authorize rollback. Ambiguous launch criteria produces thrash between engineering and stakeholders; write them down once and reuse for the next domain you onboard.

Common pitfalls and sequencing traps

Teams sometimes front-load flashy LLM tuning while skipping canonical URLs for documents, deduplication, and ACL tests—then blame the embedding model. Others perfect offline accuracy on a static snapshot while production feeds drift. Guard against both by tying releases to data version hashes and running shadow evaluations on new index builds before cutover.

Another trap is measuring only click-through happiness on generated text. Pair qualitative review with task success: did the user complete the workflow, and was every material claim tied to a retrievable source? Without that, “helpful” prose can still create liability.

Executive dashboards should include operational health alongside qualitative anecdotes: trending empty-retrieval counts often predicts user churn before NPS moves.

Finally, avoid anonymous “platform” ownership. Name a product manager for outcomes and an engineering lead for reliability. RAG without accountability becomes an unmaintained search box with a chat skin. The checklist above is a living document—reuse it at kickoff, mid-pilot, and pre-production go-live so gaps are explicit rather than discovered by end users.

Stack choices without hype

Vector databases, embedding APIs, and orchestration frameworks differ in operational maturity, not magic. Prefer components your team can operate: backup strategy, regional failover, and clear cost models matter more than marginal benchmark gains on toy datasets. If you lack vector search experience, managed offerings can reduce toil; if you have strong data platform engineers, self-hosted may be cheaper at scale.

Keep provider count understandable. Every additional SaaS boundary is another DPA, another credential rotation story, and another place latency accumulates. Standardize embedding models per language or domain to simplify reindex jobs; document the model name and dimension in your runbooks so upgrades are planned, not accidental.

Treat “LLM gateway” features—caching, routing to cheaper models for simple tasks, PII scrubbing—as first-class from week one if you expect traffic growth. Retrofitting cost controls after bills spike is slower than wiring sane defaults early. The checklist items on monitoring and rollback pair directly with these architectural choices: you should be able to freeze a bad embedding version or roll prompts forward without a full outage.

Load testing should simulate both interactive chat bursts and batch reindex jobs competing for the same cluster; capacity surprises often appear when nightly ETL overlaps with peak human usage. Capture baselines for embed throughput and query fan-out so scaling decisions are data-backed.