Build Retrieval-Augmented Generation (RAG) systems that answer questions from your private documents with accurate, citation-backed responses — no hallucinations.
Overview
Standard LLMs make things up. RAG systems ground every response in your actual documents, giving users accurate information they can trust and verify. I build end-to-end RAG pipelines from ingestion and chunking through retrieval, reranking, and final generation.
Process
Document Audit
Inventory your data sources: PDFs, databases, APIs, wikis — and identify the query types to optimize for.
Ingestion Pipeline
Parse, chunk, and embed documents using optimized strategies (semantic chunking, parent-child, late chunking).
Retrieval & Reranking
Combine vector similarity search with BM25 or cross-encoder reranking for maximum precision.
Generation Layer
Prompt-engineer the LLM to answer only from retrieved context, include citations, and gracefully handle out-of-scope queries.
Evaluation & Tuning
Measure retrieval precision, answer faithfulness, and latency — then tune until metrics hit production targets.
Benefits
Deliverables
FAQ
Retrieval-Augmented Generation combines search over your private documents with a language model. The model answers only from the passages it retrieves and cites them, so responses can be verified.
PDFs, databases, APIs, and wikis. The first step is a document audit to inventory your sources and the types of questions people ask.
No. The pipeline can run on your own infrastructure, and the deliverables include infrastructure-as-code for deploying it there.
Retrieval precision, answer faithfulness, and latency are measured against an evaluation benchmark, and the pipeline is tuned until those targets are met. The benchmark is handed over with the code.
Pinecone, ChromaDB, Weaviate, and PostgreSQL with pgvector, chosen to fit your existing stack.
Live Examples
Real projects built using this service
A context-aware medical chatbot using Retrieval-Augmented Generation (RAG) to answer questions with cited information from ingested PDFs using OpenAI, LlamaIndex, and Pinecone and embedding model fined tuned for medical documents.
A GenAI-powered AI/ML chatbot that delivers accurate, citation-backed answers using Retrieval-Augmented Generation. The system ingests curated Wikipedia articles, applies semantic chunking to preserve topic boundaries, and retrieves context through hybrid search with vector MMR and BM25. It uses LangGraph for structured conversation flow, AutoCut-style context distillation to reduce redundant evidence, SQLite-backed session persistence, and LangSmith tracing for observability. Built with FastAPI, Pydantic, ChromaDB, HuggingFace embeddings, Ollama-hosted LLMs, and a Next.js + TypeScript + Tailwind frontend, with Docker Compose support for deployment.
From the Blog
Deep-dives on the techniques behind this service
