RAG Course — Build Retrieval-Augmented Generation That Holds Up
A live RAG course for developers and committed beginners. Learn to ingest messy documents, chunk and embed them, pick a vector database, combine keyword and vector search, re-rank, cite sources and measure answer quality, then ship it behind an API with reviews from senior engineers.
Updated- Live online classes + recordings
- Senior Python developers as mentors
- Certificate with a verifiable ID
A RAG course should teach you why retrieval-augmented generation fails as much as how to build it. Wiring a PDF to a vector database and an LLM takes an afternoon; getting answers that are correct, cited and still correct after the documents change is real engineering. The Ezibell AI Academy, run by Ezibell Tech, a software company based in Jaipur, teaches that engineering live online, through mentors who build RAG systems for clients.
Retrieval-augmented generation means fetching the most relevant passages from your own data at question time and asking the model to answer from them, with references. It is how assistants answer from policies, manuals, contracts or databases without retraining any model, which is also why this is not a machine-learning course. You will not train models; you will design pipelines, choose embedding models and vector stores, and prove quality with evaluations.
Three tracks teach RAG at different depths. The 14-week AI Engineer Accelerator spends weeks four to six on embeddings, vector databases and production RAG, with evals in week eleven. The 16-week Backend programme adds RAG search with PostgreSQL and pgvector inside a real API. Beginners meet RAG in the Generative AI module of the 20-week Python to AI Launchpad. For fees and EMI options, WhatsApp +91 82097 16316.
What you learn on the RAG course
Ingestion pipelines for messy documents
Load PDFs, web pages, tables and documents, clean them, keep useful metadata such as source, section and date, and re-index when content changes. Many RAG quality problems start at ingestion, so it gets the same care as the prompt.
Chunking strategies, compared
Fixed-size, recursive, semantic and structure-aware chunking, tested side by side on thousands of documents. You see how chunk size changes what retrieval finds, and choose a strategy from measurements rather than a default copied from a tutorial.
Embeddings and vector databases
Embedding models and similarity metrics, then pgvector, Qdrant, Chroma, Pinecone and OpenSearch with metadata filtering. Think of it as a vector database course inside the programme, including the trade-off between a managed service and the Postgres you already run.
Hybrid search and query rewriting
Combine BM25 keyword search with vector similarity so exact terms like product codes and names are not lost, then improve recall with query rewriting, multi-query and HyDE. Conversational RAG adds memory so follow-up questions retrieve the right context.
Re-ranking, grounding and citations
Re-rank candidate passages with cross-encoders, keep only what earns its place in the context window, instruct the model to answer only from what it was given, and return citations a user can check. When the sources lack the answer, the system should say so.
Multi-tenant RAG, SQL RAG and GraphRAG
Keep each customer’s or department’s documents isolated and filter retrieval by the user’s permissions, so one tenant’s data never appears in another’s answer. You also build SQL RAG, text-to-SQL over a million-row database, and get an introduction to GraphRAG.
Evaluating RAG properly
Measure faithfulness, answer relevance and context precision on an evaluation dataset, combine LLM-as-judge with human review, and run regression tests whenever chunking, embeddings or prompts change. Your production RAG project ships with its own evaluation suite.
RAG in a real backend
Serve RAG through FastAPI with streaming responses, run ingestion as background jobs, control cost, latency and rate limits, and deploy on AWS. Backend learners build pgvector-powered AI search into a multi-tenant SaaS capstone reviewed by a team lead.
Choose your programme
Every track is live, mentor-led and project-based. Tap one for the full syllabus, duration and FAQ.
AI Engineer Accelerator
Embeddings and vector databases, then two weeks of RAG covering ingestion, query rewriting, re-ranking, citations, multi-tenant isolation, SQL RAG and evaluation, inside a 14-week weekend programme that also covers agents, MCP and AWS.
Python learners aiming for backend roles that include AI featuresBackend Engineering with Django & FastAPI
Sixteen weeks of Django, FastAPI, PostgreSQL, Redis and Celery, with a module on streaming LLM responses and RAG search with pgvector, ending in a multi-tenant SaaS backend with AI search.
Students and beginners who want to reach RAG from zeroPython to AI Launchpad
Twenty weeks of Python, databases and FastAPI first, then a Generative AI and RAG module where you load, chunk, embed, retrieve and answer with citations, and a study-assistant capstone over your own notes.
Chunking and embeddings: where RAG quality is decided
Chunking decides what the retriever can find, so it is the first lever to tune when RAG answers are wrong. Chunks that are too large dilute the embedding with unrelated text and waste context; chunks that are too small lose the sentence that gives a fact its meaning. Structure-aware chunking, which splits on headings, sections and table boundaries and carries the heading into each chunk, usually beats fixed character counts on real documents.
Embeddings turn each chunk into a vector so similar meanings sit close together, but no embedding model handles everything well. Domain jargon, abbreviations, code and mixed-language text can embed poorly. The practical approach is a small labelled set of questions paired with the passages that should answer them, used to compare chunking strategies and embedding models on retrieval before you touch the prompt.
Hybrid search, re-ranking and citations
Hybrid search combines keyword and vector retrieval because each fails where the other succeeds. Vector search finds paraphrases but can miss an exact invoice number, error code or person’s name; BM25 keyword search finds exact terms but misses synonyms. Running both and merging the results gives a far better candidate list, especially for business documents full of identifiers and codes.
Re-ranking then sorts that list with a cross-encoder that reads the question and each passage together, slower than vector search but much more precise. Only the highest-ranked passages go to the LLM, with instructions to answer from them and cite them. Citations are not decoration: they let users check answers, and let you see at once whether a wrong answer came from retrieval or generation.
Evaluating RAG and the failure modes to watch for
You evaluate RAG by scoring retrieval and generation separately. Context precision asks whether the retrieved passages were relevant, faithfulness asks whether the answer sticks to them, and answer relevance asks whether it addresses the question. A fixed evaluation dataset, scored by an LLM-as-judge and spot-checked by people, turns “it seems better” into a number you can track across every change.
Most production RAG failures follow a handful of patterns, and an evaluation suite catches them before users do. Diagnosing which layer failed is the core skill this course builds. Read the trace for a bad answer: if the right passage was never retrieved, fix ingestion, chunking or search; if it was retrieved but ignored or contradicted, fix ranking, context size or the prompt.
- Stale index: documents changed but embeddings were never refreshed
- Lost structure: tables and headings flattened into meaningless text
- Missed exact matches: codes and names that pure vector search cannot find
- Context overload: too many passages, so the model misses the relevant one
- Ungrounded answers: the model fills gaps from memory instead of admitting it does not know
- Tenant leaks: retrieval ignores user permissions in a shared index
How the course works
- 1Free counselling call
WhatsApp or call +91 82097 16316. Tell a mentor what you want to build with RAG and how much you code today, and they will recommend the Accelerator, the Backend programme or the Launchpad.
- 2Choose a track and batch
Pick your programme and batch. The fee, EMI options and refund and batch-transfer terms are confirmed in writing before you pay anything.
- 3Live classes, recorded
Learn in live online sessions with senior engineers and revise from recordings. Questions between classes go to the private mentor group.
- 4Build and measure
Index thousands of documents, compare chunking strategies, add hybrid search, re-ranking and citations, then score your pipeline with an evaluation suite. Mentors review your code and your results.
- 5Capstone
Deliver a RAG-powered capstone, whether a production RAG service, AI search in a SaaS backend or a study assistant over your own notes, reviewed by a team lead or senior developer.
- 6Certificate and interview prep
Your mentor signs off the work and your certificate is issued with a unique verification ID, together with the resume and interview support your track includes.
Why learn with us
What you’ll work with
What changes the fee for a RAG course
RAG is taught inside three programmes of different lengths, so the fee depends on which one fits you. The main factors:
The Accelerator covers RAG end to end, from chunking to evaluation, across several modules. The Backend programme focuses on pgvector search inside an API. The Launchpad gives beginners a two-week Generative AI and RAG module.
The Accelerator is 14 weeks with about 105 live hours, the Backend programme 16 weeks with about 120, and the Launchpad 20 weeks with about 150, because it teaches Python from zero first.
Developers with a year or more of experience go straight to the Accelerator. Python learners targeting backend roles take the Backend programme, and complete beginners start with the Launchpad.
Indexing documents and running evaluations uses a little LLM and embedding API credit. Mentors show you free credits, cheaper development models and spending limits. Your exact fee and EMI options are shared on WhatsApp.
Rag course: questions people ask
What is a RAG course?
A RAG course teaches you to build retrieval-augmented generation systems: applications that retrieve relevant passages from your own documents or databases and have an LLM answer from them with citations. Ours covers ingestion, chunking, embeddings, vector databases, hybrid search, re-ranking, citations, multi-tenant access control, evaluation and deployment, taught live online.
Do I need to know Python for the RAG course?
Yes, for the main tracks. The AI Engineer Accelerator expects at least a year as a developer, with a fast-track Python bridge for Java, Node, .NET and PHP developers. The Backend programme expects comfortable Python basics. If you have never coded, the Python to AI Launchpad teaches Python from zero and covers RAG in weeks 17–18.
Which vector databases are covered?
The Accelerator covers pgvector, Qdrant, Chroma, Pinecone and OpenSearch, with metadata filtering and hybrid BM25-plus-vector search. The Backend programme uses PostgreSQL with pgvector, and the AWS programme uses Bedrock Knowledge Bases with OpenSearch Serverless or Aurora pgvector. You learn the trade-offs, so you choose deliberately rather than by default.
Will I learn how to evaluate RAG?
Yes. You measure faithfulness, answer relevance and context precision on an evaluation dataset, use LLM-as-judge with human review, and add regression tests so quality does not slip when you change chunking, embeddings or prompts. Your production RAG project ships with its own evaluation suite, traced in Langfuse and LangSmith.
Is the RAG course online, and are there weekend batches?
Yes, it is live online with a recording of every session. The Accelerator runs on weekends for working professionals; the Backend programme and the Launchpad run on weekday evenings or weekends. Exact timings are shared before each batch starts. If you want to meet the team in person, WhatsApp us to arrange a time.
What RAG projects will I build?
On the Accelerator you build a production RAG service with hybrid search, citations and an evaluation suite, plus a text-to-SQL assistant over a million-row database. Backend learners add pgvector-powered AI search to a multi-tenant SaaS backend. Launchpad learners build a chat-with-your-notes app over their own PDFs, with citations, and a RAG study-assistant capstone.
Does the RAG course include machine learning or fine-tuning?
No. RAG improves answers by retrieving the right information at question time, without training or fine-tuning a model, and the academy does not teach machine learning, deep learning or data science. You work with existing models through APIs and focus on retrieval, grounding, evaluation and deployment, the engineering most RAG work needs.
Is there a certificate, and do you offer placement?
You receive a certificate with a unique ID verifiable at /certificates once your mentor signs off your labs and capstone. It is a professional training certificate, not a degree or accreditation. We do not guarantee placement; you get a reviewed portfolio, interview preparation where your track includes it, and introductions within our network.
Learn to build RAG that answers correctly
WhatsApp or call +91 82097 16316 with what you want to build and your current skills. A senior engineer will recommend the right track, share the current fee and EMI options, and confirm the next batch.