Live RAG Workshop
Build a RAG Chatbot from Scratch
Most RAG content explains retrieval augmented generation at a high level but never shows the full path from raw documents to a trustworthy chatbot. This workshop closes that gap. If you have been searching build RAG chatbot, RAG tutorial, retrieval augmented generation, or LangChain RAG, this session is designed to match that exact intent with a serious hands-on build.
You will build a chatbot that answers questions from your own documents using embeddings and a vector database. Just as importantly, you will learn how to avoid the failures that make many RAG demos look exciting but feel disappointing in real use: bad chunking, weak metadata, poor retrieval quality, and ungrounded answers.
Own-document Q&A system
Build a chatbot that can answer questions over PDFs, docs, notes, or internal knowledge in a grounded way.
RAG decisions explained
Understand chunking, embeddings, vector search, prompt construction, and evaluation as engineering choices.
Useful beyond tutorials
The project structure is immediately adaptable for support bots, policy assistants, or internal knowledge tools.
2 hours
Live code-first build
₹499 / $19
Geo-aware pricing
Recording included
Replay the full RAG workflow later
Search intent
Why this page is intentionally built to rank for high-intent workshop searches
Search visibility is strongest when a page resolves the real question behind a keyword. This section turns the page into a stronger resource for people actively searching for this exact topic.
Most RAG content explains retrieval augmented generation at a high level but never shows the full path from raw documents to a trustworthy chatbot. This workshop closes that gap. If you have been searching build RAG chatbot, RAG tutorial, retrieval augmented generation, or LangChain RAG, this session is designed to match that exact intent with a serious hands-on build.
You will build a chatbot that answers questions from your own documents using embeddings and a vector database. Just as importantly, you will learn how to avoid the failures that make many RAG demos look exciting but feel disappointing in real use: bad chunking, weak metadata, poor retrieval quality, and ungrounded answers.
When teams want an LLM to answer grounded questions over company documents, policies, research, or customer knowledge, RAG is still the most common answer. The people who understand retrieval quality, chunk design, and answer grounding are far more useful than the people who only know how to call a chat completion endpoint.
If you want a RAG tutorial that produces understanding instead of just another demo, this workshop is built for that standard.
Keyword focus for this workshop page
- build rag chatbot
- rag tutorial
- retrieval augmented generation
- langchain rag
- vector database tutorial
- embeddings chatbot
What you will build
What you will build in this retrieval augmented generation workshop
This is not a vague webinar page. You should know exactly what you are getting before you register. The bullets below are intentionally specific so the workshop attracts serious learners with real engineering intent.
Document ingestion pipeline
Load files, clean text, preserve metadata, and prepare a corpus that gives retrieval a real chance to work well.
Chunking strategy with intent
Compare naive splitting against structure-aware chunking so retrieval returns meaningful units instead of broken fragments.
Embeddings and vector indexing
Create embeddings, store them in a vector database, and understand what similarity search is actually doing.
Retriever with evidence
Fetch the best chunks for a question and preserve source context so answers stay auditable and grounded.
Answer generation layer
Combine retrieval with prompting to produce useful answers while reducing hallucination pressure.
Evaluation and improvement loop
Learn how to inspect failure cases and improve the RAG stack rather than hoping more tokens will save it.
1chunks = split_documents(docs, chunk_size=600, overlap=80)2vectors = embed_documents(chunks)3index.upsert(vectors)4 5def answer(question: str) -> str:6 hits = index.similarity_search(question, k=4)7 context = join_context(hits)8 prompt = render_prompt(question, context)9 response = llm.invoke(prompt)10 return format_answer(11 response=response,12 citations=extract_sources(hits),13 )This preview is representative, not decorative. The workshop is built around code, architecture reasoning, and the exact implementation details that make these workflows usable in real projects.
Outcome and social proof
Why RAG skills are still one of the most practical LLM capabilities
When teams want an LLM to answer grounded questions over company documents, policies, research, or customer knowledge, RAG is still the most common answer. The people who understand retrieval quality, chunk design, and answer grounding are far more useful than the people who only know how to call a chat completion endpoint.
Outcome
A working RAG chatbot project
You leave with a practical document-Q&A system you can demo and extend.
Outcome
RAG architecture confidence
You will understand the moving parts well enough to reason about tradeoffs instead of copying a template.
Outcome
Vector database literacy
You will understand where vector search helps, where metadata matters, and how retrieval quality shapes the product.
Outcome
Production-minded evaluation habits
You will know how to diagnose weak answers and improve the system methodically.
Practical application
How you can use Build a RAG Chatbot from Scratch after the workshop ends
The goal is not just to attend a live session. The goal is to leave with a mental model and implementation pattern you can reuse in interviews, portfolio work, internal projects, or customer-facing products.
Implementation takeaways
Document ingestion pipeline
Load files, clean text, preserve metadata, and prepare a corpus that gives retrieval a real chance to work well.
Chunking strategy with intent
Compare naive splitting against structure-aware chunking so retrieval returns meaningful units instead of broken fragments.
Embeddings and vector indexing
Create embeddings, store them in a vector database, and understand what similarity search is actually doing.
Retriever with evidence
Fetch the best chunks for a question and preserve source context so answers stay auditable and grounded.
Career and project leverage
A working RAG chatbot project
You leave with a practical document-Q&A system you can demo and extend.
RAG architecture confidence
You will understand the moving parts well enough to reason about tradeoffs instead of copying a template.
Vector database literacy
You will understand where vector search helps, where metadata matters, and how retrieval quality shapes the product.
Production-minded evaluation habits
You will know how to diagnose weak answers and improve the system methodically.
Who this is for
Who should join this build RAG chatbot workshop?
The strongest SEO pages also help visitors self-qualify quickly. These personas make it obvious whether the workshop matches your current stage and goals.
Software engineers
You want to understand how retrieval augmented generation fits into a real application, not just a notebook demo.
ML or AI engineers
You know the words already and want a compact, implementation-focused RAG tutorial that is actually useful.
Product builders
You need a chatbot over documents, policies, help centers, or research content and want the right architecture.
Job seekers building portfolios
You want a modern project involving embeddings, vector databases, and grounded answer systems.
Agenda
Detailed agenda: how the RAG chatbot session is structured
This is a detailed live workshop, not a teaser. The timeline below shows exactly how the two-hour session is structured so you can assess whether the depth matches what you need.
What makes a RAG chatbot good?
We begin with the product lens: what users actually expect, why grounded answers matter, and how weak retrieval ruins trust.
Ingest and clean the document corpus
You will see how source quality, structure, and metadata influence everything that comes later.
Chunking and embeddings
We compare chunking choices, create embeddings, and talk through why the retrieval layer succeeds or fails.
Index in a vector database
You will understand the indexing workflow and how similarity search retrieves candidates.
Generate grounded answers
We connect the retriever to the LLM, build context-aware prompts, and keep the answer tied to sources.
Inspect failure modes
This is where most tutorials stop. We stay and debug the real problems: bad chunks, bad retrieval, and ambiguous questions.
Discuss scaling patterns
We cover reranking, caching, evaluation, observability, and when to move from tutorial to production architecture.
Q&A on your use case
Bring your support bot, docs assistant, research helper, or internal knowledge base idea for direct feedback.
Prerequisites
Short prerequisites, realistic expectations
You should not have to guess whether you are ready. This section keeps the bar honest and practical.
- Comfort with Python or JavaScript basics is enough.
- No prior LangChain experience required.
- You do not need deep ML math.
- Best for people who want to understand why RAG works, not only how to wire it once.
Mid-page CTA
A fast decision checkpoint
If you want a RAG tutorial that produces understanding instead of just another demo, this workshop is built for that standard.
You get live delivery, recording access, code-first teaching, and a clear path to applying the material immediately after the workshop.
What is included
What you get beyond the live session
Good workshop pages do not stop at the headline. These are the assets and follow-through pieces that make the purchase feel durable rather than disposable.
Workshop recording
Replay the full document-ingestion and retrieval build whenever you need.
Reference code
Keep a clean RAG scaffold you can adapt to your own knowledge base.
Architecture notes
Remember the tradeoffs around chunking, embeddings, retrieval, and prompting.
Certificate of completion
A simple learning artifact for your AI engineering journey.
Live Q&A access
Ask about your own documents, vector DB choices, or product use cases.
Why now
Why this topic deserves deliberate practice right now
A good workshop is not only about the content. It is about timing. These topics are becoming more valuable in hiring, product work, and AI engineering portfolios because they sit at the intersection of implementation skill and system judgment.
2 hours
Live code-first build
₹499 / $19
Geo-aware pricing
Recording included
Replay the full RAG workflow later
This extra depth is intentional. High-intent visitors searching for a serious workshop should be able to understand the outcomes, the agenda, the delivery style, and the credibility of the instructor before deciding to register. That is how this page is structured.
Instructor
Learn from Debasish Maji
Debasish Maji built Atlassian's Rovo AI Agent and has 12+ years across Atlassian, PhonePe, and Infosys. He teaches retrieval augmented generation as an engineering system: data quality, retrieval quality, prompt discipline, and product trust all matter together.
Why this matters
12+
Years of engineering depth across product, backend, and AI systems.
Rovo
Built Atlassian's Rovo AI Agent, bringing direct practitioner credibility.
PhonePe
Experience in high-scale engineering environments where reliability matters.
Infosys
Strong software-delivery foundation that shows up in how the material is taught.
Built Atlassian's Rovo AI Agent
That gives this workshop unusual credibility around real-world AI system behavior.
12+ years of engineering depth
You get explanations grounded in production reliability, not only experiments.
Focus on architecture judgment
The workshop emphasizes why choices matter, not just which library call to use.
Clear code-first teaching
Every section is designed to be immediately useful for builders.
FAQ
Questions people ask before registering
Each answer below is also marked up as JSON-LD FAQ schema so both humans and search engines understand exactly what this page resolves.
Final CTA
Reserve your seat and build a grounded RAG chatbot you can actually explain, improve, and reuse.
This workshop page is intentionally detailed because serious learners want substance before they buy. If the depth here matches what you were hoping to learn, the live session will be even more valuable.