// SERVICE MODULE: RAG PIPELINE DEVELOPMENT
📚 RAG Development Services (Retrieval-Augmented Generation)
I build production RAG systems — the difference between a demo that impresses and a pipeline that answers correctly at scale. Chunking strategy, embedding choice, retrieval quality, reranking, and evaluation are where RAG succeeds or fails, and it's where I focus.
⚠ The Problem
LLMs don't know your data, and fine-tuning is expensive and stale the moment your docs change. Naive RAG (split text, embed, top-k) works in demos and falls apart on real questions: wrong chunks retrieved, context windows wasted, answers that sound right but aren't grounded.
✓ The Solution
I design RAG pipelines around your actual corpus: structure-aware chunking, hybrid retrieval where needed, metadata filtering, reranking, and grounded prompting with citations. Every build includes a retrieval evaluation set so quality is measured, not guessed.
// Technologies Used
// What You Get
- ▸Corpus analysis and chunking/embedding strategy
- ▸Ingestion pipeline with incremental updates
- ▸Retrieval + generation API (FastAPI) with citations
- ▸Evaluation harness (retrieval accuracy, answer groundedness)
- ▸Deployment and knowledge-update runbook
// Related Work
Sentinel AI: LLM Red Teaming Framework
A human-centric AI safety system designed to evaluate and improve the robustness of Large Language Models (LLMs) through adversarial attacks, alignment checks, and safety mechanisms.
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// Frequently Asked Questions
?What is RAG and when do I need it?
Retrieval-Augmented Generation lets an LLM answer using your documents: the system retrieves the most relevant content and the model generates an answer grounded in it. You need RAG when answers must reflect your knowledge base, product docs, policies, or any data the model was never trained on.
?Which vector database do you recommend?
It depends on scale and hosting: ChromaDB is excellent for self-hosted and mid-size corpora; Pinecone for managed scale; pgvector when you want everything inside PostgreSQL. I've worked with all three and will recommend based on your constraints, not fashion.
?How do you measure RAG quality?
With an evaluation set built from real user questions: retrieval hit-rate, answer groundedness against sources, and regression tests that run before any prompt or index change ships. I wrote about this in my production RAG article.
// From the Blog
// Related Services
Ready to build?
Tell me about your project and I'll reply with a concrete plan, timeline, and quote — usually within 24 hours.