Notifications
Clear all
Topic starter
19/08/2026 3:40 pm
TL;DR: Vector database choice affects retrieval speed, filtering, and hybrid search in RAG systems, according to Braintrust, but it cannot determine whether the generated answer is actually grounded in the retrieved context. That separation between retrieval quality and answer quality means teams need evaluation after search, not just infrastructure selection.
NHIMG editorial — based on content published by Braintrust: Best vector databases for RAG in 2026
Questions worth separating out
Q: How do I know if my RAG retrieval is actually good?
A: Good retrieval is not just finding similar chunks.
Q: When should organisations choose a managed vector database over self-hosted search?
A: Choose managed when the team wants less operational burden around scaling, backups, and monitoring.
Q: What breaks when RAG systems filter documents only by metadata?
A: Metadata-only filtering breaks when access rules are too dynamic to encode in every record.
Practitioner guidance
- Define retrieval boundaries before indexing Map tenant, source, date, and permission fields into the vector store before production use so retrieval cannot surface context outside the intended boundary.
- Test hybrid search against exact-term failures Build cases with policy names, error codes, document IDs, and regulated terms to confirm that semantic retrieval and keyword matching both return the right chunks.
- Evaluate grounding separately from retrieval Measure whether the retrieved chunks were relevant and whether the answer stayed supported by those chunks, instead of treating top-k retrieval as success.
What's in the full article
Braintrust's full article covers the implementation detail this post intentionally leaves at the architecture level:
- Side-by-side feature comparison of Pinecone, Weaviate, Qdrant, Chroma, and Turbopuffer for production RAG selection
- Hosting model trade-offs for managed, self-hosted, local, and BYOC deployments
- Use-case guidance for hybrid search, filter-heavy retrieval, and large-scale object-storage search
- Practical considerations for teams deciding whether pgvector is enough or a dedicated vector database is justified
👉 Read Braintrust's guide to the best vector databases for RAG in 2026 →
Vector databases for RAG in 2026: what matters beyond retrieval?
Explore further
19/08/2026 4:19 pm
Vector database selection is now a governance decision, not just an infrastructure choice. In RAG systems, the retrieval layer shapes what context the model can see, which means it influences downstream decision quality and disclosure risk. For identity and security teams, that matters when retrieval is scoped by permissions, tenancy, or source trust. The right lens is governance of context exposure, not only search performance.
A question worth separating out:
Q: What is the difference between hybrid search and pure vector search?
A: Pure vector search finds semantically similar chunks, even when the wording differs from the query. Hybrid search combines semantic matching with keyword search, which helps when exact strings matter, such as policy names, error codes, document IDs, and compliance terms.
👉 Read our full editorial: Vector database choice shapes RAG retrieval, but not answer quality
