The RAG architecture decision guide
Make retrieval-augmented generation decisions with less guesswork. This guide organizes the tradeoffs, failure modes, and architecture patterns teams need before production rollout.
What's covered
A complete decision framework for production RAG systems
When (and when not) to use RAG
Decision framework for knowing if RAG is the right solution for your use case
Vector database selection
Side-by-side comparison of Pinecone, Weaviate, Qdrant, ChromaDB, and pgvector
Chunking strategies
How to split documents for optimal retrieval—sizes, overlaps, and semantic boundaries
Embedding model selection
OpenAI, Cohere, and open-source options: performance, cost, and latency tradeoffs
Retrieval optimization
Hybrid search, re-ranking, and metadata filtering for better results
Production architecture patterns
Caching, scaling, monitoring, and cost optimization in production
Clarify the decisions that make or break a RAG build.
The strongest RAG plans start with concrete operating questions, not a database shortlist. Use these checkpoints to separate architecture decisions from vendor preferences.
Fit
Confirm whether retrieval is the right pattern before adding indexing, embeddings, and evaluation work.
Corpus
Map source quality, update frequency, access rules, and document structure before chunking begins.
Quality
Define how answers, citations, retrieval misses, and human review will be evaluated after launch.
Operations
Plan monitoring, cost controls, fallback behavior, and governance before users rely on the workflow.
| Database | Best for | Operating model | Hosted option |
|---|---|---|---|
| Pinecone | Managed vector search evaluation | Managed service | Hosted option available |
| Weaviate | Hybrid search and schema flexibility | Managed or self-hosted | Hosted option available |
| Qdrant | Performance and cost-control evaluation | Managed or self-hosted | Hosted option available |
| ChromaDB | Prototype and local workflow testing | Self-hosted / local | No hosted option |
| pgvector | Postgres-native retrieval workflows | Postgres extension | No hosted option |
Full guide includes evaluation prompts, cost-modeling worksheets, and architecture review checklists
Make retrieval quality visible before users depend on it.
A RAG system earns trust when teams can inspect what was retrieved, why an answer was generated, and where review is still required. KOBA42 frames every architecture review around observable evidence rather than vendor claims.
Corpus readiness
Document owners, update cadence, access rules, stale-content handling, and excluded sources are defined before indexing.
Evaluation set
Representative questions test citation accuracy, retrieval misses, conflicting sources, and no-answer behavior.
Review path
Human review, monitoring, escalation, and rollback expectations are written down before the workflow reaches production users.
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Pressure-test your RAG plan before the build gets expensive.
Share the decision points you are weighing—data sources, retrieval approach, evaluation criteria, and rollout constraints—and KOBA42 can help turn them into a practical architecture review.
Built for technical teams
Engineers
Building your first RAG system and need to make architecture decisions that won't bite you later
Tech leads
Evaluating vector databases and need a framework to present to stakeholders
CTOs / Architects
Setting technical direction and need practical architecture patterns, not tutorials
Grounded in production architecture work
This guide is written for teams making practical build-versus-buy, retrieval quality, data governance, and operating-model decisions—not for toy demos or unchecked benchmark chasing.
- DesignArchitecture decisions before implementation
- EvaluateRetrieval quality, risk, and operating costs
- OperateMonitoring, access patterns, and review loops
