๐ช๐ต๐ ๐ ๐ ๐๐ถ๐ฟ๐๐ ๐ฅ๐๐ ๐๐ฎ๐๐ฒ๐ฟ ๐ฆ๐๐ฎ๐ฟ๐๐ ๐ถ๐ป ๐ฃ๐ผ๐๐๐ด๐ฟ๐ฒ๐
Many people rush to pick a vector database when building RAG workflows.
They ask the wrong first question.
The right question is: What approved knowledge should the workflow use before an AI makes a decision?
In operational AI, models should not rely on raw memory. The context you need already exists in your systems. This includes:
- Approved response rules
- Handoff criteria
- Product notes
- Campaign guidance
- Past human decisions
Most of this data is already relational. You have leads, workflow names, stages, and logs.
The real challenge is not writing text. The challenge is retrieving the right context and showing which source influenced the decision.
I start my retrieval layer in Postgres with pgvector.
This choice keeps everything in one place. You can store:
- Documents and chunks
- Metadata and approval requirements
- Retrieval traces
- Cost estimates
- Human review outcomes
A retrieval layer is not real until it has failure criteria. You must be able to answer:
- Did the correct source appear in the top results?
- Did the system hand off when the source was missing?
- Did the language come only from allowed chunks?
- Can I review which chunks influenced the decision?
A RAG layer looks smart even when it pulls the wrong context. You need to link the retrieval step and the AI decision step.
You need a single view that shows:
- Chunk IDs and similarity scores
- Model names and token costs
- The final decision and handoff reasons
Standalone vector databases have a place. Use them if you need massive search traffic or complex filtering.
But for revenue and operations, prioritize control and auditability.
Starting with Postgres gives you:
- Proximity to operational data
- Easier tracing
- Easier evaluation
- Better human-in-the-loop control
Focus on schema clarity instead of architectural novelty.
Optional learning community: https://t.me/GyaanSetuAi