vishal patel
← Case studies
Tech lead · builder

AI-augmented engineering: setup agent, RAG knowledge app and LLM migration tooling

Three AI builds that removed real friction: a Claude-driven local setup agent for 60+ microservices, a RAG app over internal engineering docs, and an LLM pipeline that turns a legacy website into a CMS import package.

ai-agentsragllmdeveloper-experiencemigration
Local environment for 60+ services provisioned by typing "cms setup"
Grounded answers over internal docs with Pinecone retrieval
Legacy site → content model → import package, automated

1 · Developer setup agent (team build)

Problem: a new developer had to clone dozens of repos and hand-configure MongoDB, Redis and other dependencies. Onboarding took a long time and broke often.

diagram

Outcome: now used by all developers on the team. It's an agent with real tools and a validation loop, not a chat wrapper.

2 · Engineering knowledge RAG (built by me)

Internal engineering docs are chunked and embedded into Pinecone, and an LLM answers from the retrieved context. The goal was fewer repeated "where is this documented?" questions and faster onboarding. Architecture details are in RAG Architecture.

3 · LLM-assisted content migration (built by me)

diagram

The key design choice: the LLM proposes and deterministic code validates. The model infers content types and fields; code enforces the target schema and emits the import format. The fuzzy part gets AI, and correctness stays in code.

What ties them together

  • Treat AI features as systems: tools, validation, retrieval, failure handling.
  • Put AI where the work is fuzzy (understanding a site, interpreting logs) and keep code where the work must be exact (schemas, provisioning).
  • Measure adoption. The setup agent spread across the team because it worked.