Retrieval Pipeline

As one of the first engineers at the company, I built the retrieval-augmented generation pipeline behind our product’s assistant — the part that grounds model answers in a customer’s own documentation and operational data instead of letting the model guess.

The problem

RAG demos are easy and RAG in production is not. The gap is everything that a toy example ignores: documents that are messy, inconsistent, and constantly changing; queries that are vaguer than any benchmark; retrieval that returns plausible chunks rather than correct ones; and a model that will confidently stitch together a wrong answer from almost-right context. When the output feeds real decisions, “usually fine” isn’t a bar you can ship against.

The approach

I owned the pipeline end to end: ingestion and chunking, embeddings and the vector store, the retrieval layer, and the prompt assembly that hands grounded context to Gemini on Vertex AI. The work that mattered most wasn’t the happy path — it was making failure visible. That meant building evaluation into the loop so a change to chunking or retrieval could be measured rather than eyeballed, and treating retrieval quality as the thing to optimize, since a strong model on weak context still produces weak answers.

Garbage retrieval, confident answer. Most of the engineering in production RAG is making sure the model is arguing from the right evidence.

What came out of it

A retrieval system that became core infrastructure for the product. {Add specifics here — corpus size, the eval metrics you track, a latency or accuracy improvement, or how it changed what the product could promise customers.}