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AI2023 - 2024

Multiple RAG Systems

A family of RAG pipelines tailored per domain (support, legal-style docs, ops runbooks).

Architecture & workflows

How this product is built, deployed, and operated in production.

Delivery flow

End-to-end delivery flow for this project, from intake through the final handoff.

Challenge

One generic RAG setup underperformed across different document types and access rules.

How we did it

Templated ingestion and evaluation packs per domain, sharing the same service skeleton.

Outcome

Faster rollout of new knowledge bases with consistent quality gates.

Tech stack

PythonLangChainLlamaIndexVector DBFastAPI

Step-by-step

  1. 1

    Domain pack

    Chunking + metadata rules per corpus

  2. 2

    Build index

    Embeddings + filters for tenant/role

  3. 3

    Eval set

    Golden questions score groundedness

  4. 4

    Serve

    Query API with citations

  5. 5

    Monitor

    Track failures and refresh stale docs

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