Natural Language Processing (NLP)
NLP covers the full arc of language processing: from tokenisation and classical statistical methods through word embeddings to transformer-based models.
2 months Classroom & Online
View syllabus 6 modules
AI & Advanced Technologies
Retrieval Augmented Generation (RAG)
Ground the model in your own documents.
RAG is the pattern behind almost every enterprise AI deployment: retrieve the right context from your own data, then let the model answer from it. It is also the pattern most teams implement badly, and retrieval quality is almost always the reason.
6 weeks Classroom & Online Intermediate to Advanced 6 modules
RAG Ground the model in your own documents.
Overview
What this course is, plainly
RAG is the pattern behind almost every enterprise AI deployment: retrieve the right context from your own data, then let the model answer from it. It is also the pattern most teams implement badly, and retrieval quality is almost always the reason.
This track goes deep on the retrieval half — chunking strategy, embedding choice, hybrid search, reranking and evaluation — and then on the generation half, with citation, grounding checks and honest failure handling.
Why choose this course
- Retrieval quality treated as the main problem
- Vector, keyword and hybrid search compared
- Reranking and query rewriting techniques
- Citation and grounding verification
- Measurable evaluation with a real test set
Curriculum
Module-by-module syllabus
The same structure on every Xypron course page, so you can compare tracks like for like. Each module ends in working code, not a quiz.
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Module 1: RAG Fundamentals
Why retrieval beats fine-tuning for most knowledge problems.
- The RAG architecture end to end
- RAG vs. fine-tuning vs. long context
- Failure modes of naive RAG
- Document ingestion pipelines
- Choosing a use case that will actually work
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Module 2: Chunking & Embeddings
The two decisions that determine everything downstream.
- Fixed, recursive & semantic chunking
- Chunk size, overlap & metadata design
- Embedding model selection & benchmarks
- Multilingual & domain-specific embeddings
- Embedding cost and refresh strategy
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Module 3: Vector Databases
Storing and searching millions of vectors quickly.
- FAISS for local indexes
- Chroma, Qdrant & Pinecone
- Approximate nearest neighbour algorithms
- Metadata filtering & hybrid queries
- Index maintenance & incremental updates
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Module 4: Advanced Retrieval
The techniques that separate a demo from a deployment.
- Hybrid search: BM25 + dense vectors
- Cross-encoder reranking
- Query rewriting & expansion
- Parent-document & hierarchical retrieval
- Multi-hop and agentic retrieval patterns
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Module 5: Generation & Grounding
Making the answer trustworthy.
- Context assembly & token budgeting
- Citation and source attribution
- Grounding verification & refusal behaviour
- Handling conflicting sources
- Streaming answers with references
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Module 6: Evaluation & Production
Proving the system works, then keeping it working.
- Building a retrieval test set
- Metrics: recall@k, MRR, faithfulness, answer relevance
- RAGAS and evaluation tooling
- Latency, caching & cost control
- End-to-end capstone project
Projects you'll build
- Company policy assistant with cited answers
- Hybrid-search knowledge base over technical documentation
- RAG evaluation harness with a labelled question set
Roles this prepares you for
- AI Engineer
- RAG / Search Engineer
- LLM Application Developer
- Solutions Engineer — AI
Tools you'll master
Python LangChain LlamaIndex FAISS Chroma Qdrant Hugging Face RAGAS Outcomes
What happens after the last module
The course does not end at the capstone. Placement support runs from your first mock interview until you have an offer — and it is support, not a guarantee.
Resume rewrite
Rebuilt around the projects you actually shipped here, targeted at the roles you are applying for.
Mock interviews
Technical and HR rounds with written feedback after each one, repeated until the gaps close.
Profile sharing
Your profile goes to our hiring partners with a note from the trainer who taught you.
Interview scheduling
We book the interviews and follow through until there is a decision either way.
Next step
Ask us anything about Retrieval Augmented Generation (RAG)
Batch timings, the fee, whether your background fits, what the trainer's day job is — all fair questions. A counsellor will call you back within two business hours.
Python fluency and basic familiarity with LLM APIs. The LLM track is the recommended precursor.
The course runs for 6 weeks in classroom & online mode. Weekday, weekend and evening batches are scheduled continuously — call +91 73493 01293 for the next available start date.
Company policy assistant with cited answers; Hybrid-search knowledge base over technical documentation; RAG evaluation harness with a labelled question set — plus an end-to-end capstone project you deploy yourself and can walk an interviewer through line by line.
Yes. Sit a full session with the trainer who would actually teach your batch before you decide anything. Ask for one on WhatsApp or through the enquiry form on this page.
Enquiry
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Also in AI & Advanced Technologies
Compare with the neighbouring tracks
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Next step
Ready to start Retrieval Augmented Generation (RAG)?
Sit a free demo class before you commit. Ask about batch timings, the trainer, and exactly what the placement support covers.