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AWS & KNOWLEDGE AI

AWS RAG architecture: a production planning checklist

Retrieval-augmented generation (RAG) can bring approved business knowledge into an AI application's answers. It is not a guarantee of factual accuracy, security or useful business outcomes. Before choosing a managed knowledge service or a custom pipeline on AWS, define the users, knowledge boundary, evaluation cases and operating responsibilities. This is an architecture planning checklist, not a benchmark or a fixed deployment recipe.

By Truvenix · Practical planning guide · Updated

1. Decide what the assistant must know and do

Begin with a narrow knowledge task: for example, helping staff locate an approved operating procedure. Separate answering a question from taking an action in another system. Retrieval may be useful when the answer depends on changing documents, but a structured database query or deterministic workflow may be more appropriate for exact records and routine calculations.

  • Name the users, knowledge owners and permitted actions.
  • List questions that should be answered, declined or escalated.
  • Identify authoritative sources and who resolves conflicting information.
  • Keep sensitive production documents out of public discovery forms.

2. Treat ingestion as an operating workflow

A useful index needs more than an initial upload. Plan document parsing, chunking, metadata, freshness and deletion handling. A stale policy retrieved confidently is still a failure. Use representative documents to compare retrieval behaviour rather than selecting a chunk size or embedding model by habit. Verify supported regions, models and connectors against current AWS documentation before committing to the design.

  • Track source identity, version, owner and last approved update.
  • Test tables, scanned material and conflicting passages where relevant.
  • Define re-indexing triggers and the removal of revoked documents.
  • Keep a reproducible evaluation sample separate from production content.

3. Design the retrieval and generation boundary

A typical flow authenticates the user, retrieves permitted passages, builds a bounded prompt and returns an answer with source context. Managed knowledge services and customer-managed stores have different responsibilities. Do not assume that placing documents in one index enforces every user's permissions. Treat retrieved text as untrusted input and keep tool permissions independent from model instructions.

  • Enforce appropriate tenant and document access before returning context.
  • Validate source references and allow the assistant to say it lacks evidence.
  • Keep write actions behind scoped integrations and human approvals where needed.
  • Record the retrieval settings, prompt version and model configuration used in tests.

4. Evaluate retrieval separately from answer quality

When an answer is wrong, identify whether the right evidence was absent, poorly retrieved or misused during generation. Test incomplete questions, outdated sources, cross-tenant requests and malicious instructions inside documents. Compare configurations against the same representative questions. A convincing demonstration is not evidence that the system meets production requirements.

  • Check whether the expected source appears in retrieved context.
  • Review groundedness, relevance, uncertainty and citation usefulness.
  • Test permission boundaries and unsupported requests as release blockers.
  • Have the business owner approve the evaluation criteria and unresolved limitations.

5. Plan latency, costs and recovery

Estimate the full operating path: ingestion, storage, retrieval, model usage, application hosting and monitoring. Use workload assumptions and measured tests rather than a universal cost-per-answer claim. An application needs appropriate timeouts, limits, failure responses and a way to recover when a provider or source is unavailable. Retain only the logs appropriate to the business and deployment requirements.

  • Define expected usage, peak load and acceptable response time.
  • Set budgets, usage limits and accountable incident ownership.
  • Test source outages, model failures and deployment rollback.
  • Hand over account access, deployment instructions, evaluation material and support boundaries.

Platform reference

Check the current provider rules for your deployment. This planning guide does not establish legal or platform compliance.

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