Skip To Main Content
Suman Nath on Muck Rack

Suman Nath

As seen in: MSN India, MDPI, Taylor & Francis Online, The Indian Express, Firstpost, Outlook (India), The Quint, DEV Community, The Wire (India), ThePrint and
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Suman

Contact Suman, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Anatomy of a Full RAG Application: Every Concept, One Self-Hosted Stack

|
By Suman Nath
| DEV Community
"Chat with your documents" sounds simple. Then you build it, and you discover a good RAG system is really eight systems wearing a trench coat. I recently finished myRAG — a fully self-hosted RAG stack: FastAPI backend, React frontend, and three storage engines (Qdrant, PostgreSQL, Neo4j), all orchestrated with Docker Compose. This post walks through every stage of the pipeline and the concept behind it, with real code from the project.
Open in Who Shared Issue with article?

Practical RAG, Part 1: The Simplest RAG That Actually Works

|
By Suman Nath
| DEV Community
By Suman — Part 1 of the **Practical RAG* series. All code is in a runnable notebook: https://www.kaggle.com/code/sumannath88/ep01-simple-rag Everyone talks about RAG. Far fewer people have built the simplest version end to end and looked at exactly where it falls over. That's what this series does. We start with the most naive RAG pipeline that actually works, understand it completely, and then — one concrete problem at a time — make it better. No frameworks hiding the moving parts.
Open in Who Shared Issue with article?

LLM-as-a-Judge: I Built One From Scratch, Then Checked It Against Humans

|
By Suman Nath
| DEV Community
In Part 1 the model's job was to pick one of 77 labels, so I could check it with ==. But most real LLM output isn't like that — it's a paragraph, a summary, a support reply. There's no label to compare against. So people reach for the obvious move: use an LLM to grade the LLM. Show it a question and an answer, ask "how good is this, 1–10?", trust the number. It works shockingly well... right up until it doesn't, in ways that don't show up unless you go looking.
Open in Who Shared Issue with article?
See all 48 licensed articles (58 Total)