RAG resource

RAG Implementation Checklist - Production Retrieval Without Surprises

RAG demos fail because retrieval, evaluation, and security are afterthoughts. This checklist walks through every step we run on real deployments so your ingestion pipelines, vector stores, rerankers, and guardrails hold up under production traffic.

Prereqs before you start

Most RAG failures come from skipping groundwork. Confirm these before building pipelines.

Data map: know every source (docs, tickets, product DB, wikis) and who owns it.
Access model: tenants, roles, and redaction rules agreed with security/compliance.
Success metrics: relevance, citation accuracy, latency, cost targets, and SLAs.
Budget guardrails: token/embedding cost ceilings, fallbacks, and kill-switches.
Incident playbook outline: who gets paged for leaks, poisoning, or degraded answers.

Ingestion & normalization

Source inventory: PDFs, HTML, databases, APIs, code, structured data documented.
Normalization pipeline defined (cleaning, parsing, metadata enrichment, classification).
PII/PHI detection and redaction rules enforced before embeddings.
Versioning strategy (content hashes, etags) so stale docs exit retrieval.
Backfill/real-time ingestion modes with retry + alerting.

Chunking & embeddings

Content-specific chunking heuristics (semantic, token-based, hybrid) documented.
Metadata schema finalized (tenant, ACL, tags, timestamps, lineage).
Embedding model selection vs latency/cost tradeoffs with fallback plan.
Similarity thresholds tuned using offline evals (precision/recall targets).
Poisoning safeguards: content hashing, anomaly detection, approval workflows.

Retrieval & reranking

Hybrid retrieval strategy (vector + keyword/BM25) with feature flags.
Reranker selection (Cross-encoder, ColBERT, BGE rerank) plus latency budget.
K-value + context window sizes tuned for different queries.
Tenant isolation checks (RLS, metadata filters, runtime assertions).
Streaming + pagination patterns validated for large responses.

Evaluation & monitoring

Offline eval harness with golden datasets, rubric scoring, and regression alerts.
Human-in-the-loop review workflow for low-confidence answers.
Online metrics: answer relevance, citation accuracy, latency, cost per query.
Prompt injection/adversarial suites replayed in CI + nightly jobs.
Telemetry to PostHog/Sentry with structured context for each retrieval.

Security & deployment

Penetration testing scope includes RAG-specific cases (poisoning, citation spoofing, tenant leaks).
Secrets, API keys, and model credentials managed via vaulted services.
CI/CD pipeline with automated evals, adversarial prompts, and rollback plans.
Incident response playbook for hallucinations, data leaks, and degraded embeddings.
Backup/restore plan for vector indexes and document stores (snapshots, RPO/RTO).

Cost & runway

Token + embedding spend forecast per tenant/feature with alert thresholds.
Tiered storage (hot vs warm vs archive) for embeddings and raw documents.
GPU/batch inference plan for rerankers vs serverless CPU for retrieval.
Usage-based billing or internal chargeback mapped to query metrics.
Vendor switch criteria (latency, accuracy, security terms) defined upfront.

Common failure modes (and fixes)

Cross-tenant retrieval: enforce tenant filters before similarity search and assert at runtime.
Stale content: content hashes + etags; re-embed on change; purge vector records on delete.
Poisoned docs: quarantine pipeline with human approval, anomaly detection on embeddings.
High latency: precompute embeddings, cache reranker results, tune K + context window by query type.
Hallucinated citations: verify citations against retrieved IDs; block responses when confidence is low.
Runaway cost: budget alarms per tenant/feature; cap K and max tokens per route; batch reranking.

Ownership & handoff

Data eng owns ingestion, normalization, and lineage.
App/backend owns retrieval orchestration, prompts, and API contracts.
Security owns RLS, secret management, and adversarial testing.
Product/UX owns guardrails surfaced to users (retry, clarification, feedback flows).
Infra/SRE owns observability, budgets, and incident response.

Step-by-step build plan

A practical order of operations to avoid rework and keep security aligned with delivery.

Inventory + access: sources, owners, ACLs, redaction rules, compliance requirements.
Schema + metadata: finalize fields (tenant, ACL, tags, timestamps, lineage) before embedding.
Chunking strategy: semantic vs token vs hybrid; benchmarks for each content type.
Embedding + storage: choose models and vector DB; set namespace strategy; plan re-embedding.
Hybrid retrieval: implement keyword + vector; feature-flag rerankers; tune K/context per query class.
Guardrails: prompt templates, policy checks, citation validation, tenant assertions, cost budgets.
Evaluation: offline golden set, rubric scoring, adversarial suites, regression gates in CI.
Observability: structured logs/traces with tenant + document IDs; dashboards for latency/cost/accuracy.
Incident playbooks: hallucination, leak, poisoning, stale content, cost overrun; owners + steps.
Rollout: staged traffic, feedback loops, and periodic re-evals as content/models change.

Make it production-ready

Before launch, ensure these controls are in place.

Tenant isolation assertions at API, retrieval, and output levels; tests in CI to prove it.
Adversarial prompt suite (prompt injection, data exfil, jailbreaks) replayed on every deploy.
Offline eval thresholds gating releases; automatic alerts on regression.
Cost + latency budgets per feature; SLOs and alerts wired into dashboards.
Replayable traces for any response: request, retrieved docs, scores, prompt, model params, output.
Kill-switches for specific models, indexes, or tenants; rollback plan for embeddings.
Runbooks for leakage, poisoning, hallucination, and stale content incidents.

Download the PDF & Notion template

Grab the editable template if you want to track status, owners, and scores across your RAG workstreams.

No spam-just architecture resources and security updates.

Need help shipping this?

We build and pen test RAG systems for fintech, SaaS, and enterprise teams. If you want a senior engineer to run this checklist with you-and then implement it-let's talk.

Discuss your RAG build See RAG platform services

Related resources

RAG Architecture Guide — full design patterns for hybrid retrieval and semantic search.
Next.js AI Platform Development — hands-on builds for RAG + multi-tenant platforms.
AI Platform Security Guide — platform-wide controls, tenancy, and observability.
Multi-tenant SaaS Checklist — align RAG with RLS and isolation.

FAQ

Who uses this checklist?

Teams building or upgrading RAG pipelines who need a structured way to validate ingestion, retrieval, evaluation, and security workstreams.

FAQ

Why ungated?

We want the content to rank and help teams immediately. Opt in only if you want the PDF/Notion template with scoring columns and automation hooks.

FAQ

Does it tie into your delivery?

Yes-this is the same worksheet we run when building or pen testing RAG systems for clients so engineering and security stay aligned.