Retrieval-Augmented Generation (RAG): Build a Production-Grade RAG System

RAG is a technique that gives a language model access to your own documents at query time. Instead of answering only from training memory, the model retrieves relevant passages from your data and answers using them, which reduces hallucination and lets the system use information it was never trained on.

A complete retrieval pipeline over your own documents: ingestion for PDFs, web pages and markdown, a chunking strategy, embeddings in a ChromaDB vector store, hybrid search with re-ranking and metadata filtering, and measured evaluation scores using RAGAS.

Rs 1,999, inclusive of 18% GST. That covers all 20 lessons, the graded capstone project with written feedback, and doubt-solving chat support for one month. There is no subscription and nothing additional is charged at checkout.

No. You need comfortable Python and ideally one prior experience calling an LLM API. The course does not require deep learning theory, model training, a GPU or paid API keys, because the lessons use open-source embedding models and a local ChromaDB instance.

Fully self-paced and online. There are no batch timings and no scheduled classes. You start whenever you buy and move at your own speed. If you want live instructor-led teaching, our career programs are the better option.

You build a production-style RAG assistant over a real document set, then prove it works. You submit the pipeline plus RAGAS evaluation scores measured before and after your changes, and a short report explaining what you changed and why the scores moved. You receive written feedback.

You get access to a doubt-solving chat window for one month, counted from your first login rather than from the date of purchase, so buying early and starting later costs you nothing. Questions are answered within 1 working day. It is intended for course and project questions rather than unrelated consulting work.

LangChain for chains, retrievers and document loaders, ChromaDB as a local vector database, and the RAGAS framework for evaluating retrieval quality. The course also compares open-source and proprietary embedding models so you can choose deliberately.

Both, and evaluation is treated as a first-class topic rather than an afterthought. There is a dedicated lesson on evaluating RAG pipelines with RAGAS, and the graded project requires you to report scores and explain what moved them.

Hybrid search combines dense vector retrieval with sparse keyword retrieval. Dense search captures meaning while sparse search catches exact terms such as product codes and names. Most real document sets need both, which is why it is a lesson and a project requirement.

Yes, as the final lesson. It introduces how retrieval extends into agentic systems that use tools and memory, which is the direction most production RAG work is moving. It is an introduction rather than a full agents course.

People who have never written Python, people looking for a no-code tool, and people wanting deep learning theory or training models from scratch. It is also not the right choice if you specifically want live scheduled classes.

Payment is processed securely through Razorpay. Access is granted immediately after successful payment, so you can begin the first lesson the same day.

Fireblaze AI School, an AI education institute founded on 31 October 2017 that has trained more than 20,000 learners. The self-paced courses are the online, India-wide arm of the same teaching approach used in our career programs.

Yes. You receive a certificate of completion from Fireblaze AI School once you finish the course and submit the graded capstone project. The project is the part worth showing in an interview, but the certificate is included.

There is no expiry date. You keep access for as long as we run the learning platform, so you can revisit lessons later when you are building something similar at work. There is no subscription and no renewal.

Yes. Message us on WhatsApp using the link on this page and we will answer questions about prerequisites, the project or the support window before you enrol.