RAG Development
I build production retrieval-augmented generation (RAG) systems — ingestion, chunking, vector search, reranking, and grounded answers with citations.
RAG (retrieval-augmented generation) lets an LLM answer from your own documents instead of guessing. I build the full pipeline — ingest, chunk, embed, retrieve, and generate grounded answers — so your chatbot cites real sources instead of hallucinating.
What you get
- ›Document ingestion pipelines (PDFs, knowledge bases) with cleaning and chunking
- ›Embedding and vector storage with Qdrant or pgvector
- ›Retrieval with metadata filtering, and hybrid (keyword + vector) search where it helps
- ›Reranking and confidence thresholds to cut hallucinations
- ›Grounded answers with source citations and graceful fallbacks
- ›Integration into a web frontend with streaming responses
How I approach it
- 01Understand the corpus. Look at the actual documents and the questions users will ask — both drive chunking and retrieval choices.
- 02Build the retrieval core. Ingest, chunk, embed, and store; tune retrieval so the right passages come back before any LLM is involved.
- 03Ground the generation. Prompt the model with retrieved context, add citations, and set thresholds so it abstains instead of inventing.
- 04Evaluate and harden. Measure retrieval quality and groundedness, then handle timeouts, retries, and inference failures for production.
Frequently asked questions
What is RAG development?
RAG development is building the pipeline that retrieves relevant passages from your own data and feeds them to a language model so its answers are grounded in your documents. It is the standard way to give an LLM access to private or up-to-date knowledge without retraining the model.
Have you built RAG systems in production?
Yes. I engineered a production RAG chatbot using the OpenAI API and the Qdrant vector database for context-aware information retrieval, and built EigenTalk, a RAG-powered research assistant with a Next.js frontend for document ingestion and AI-powered retrieval.
How do you stop a RAG chatbot from hallucinating?
Most hallucinations are a retrieval problem, not a model problem. The fixes are better chunking, stronger embeddings, reranking the retrieved passages, setting a confidence threshold so the system abstains when it has no good context, and requiring citations so answers stay tied to sources.
Which vector database do you use?
It depends on the project. Qdrant is a strong default for filtered vector search; pgvector is ideal when the data already lives in PostgreSQL and the corpus is moderate in size. The choice follows the scale, the filtering needs, and the existing infrastructure.
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