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COURSE

RAG & AI Knowledge Systems

Build retrieval-augmented generation systems that answer questions from trusted private knowledge.

PythonEmbeddingsVector DatabasesRAGLLM Evaluation
Admissions Open
₹29,999 Indicative fee — update before production launch

Duration10 Weeks
FormatOnline / Classroom
LevelIntermediate → Advanced
ScheduleWeekday / Weekend batches
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Course overview

Learn the architecture behind knowledge-grounded AI assistants. Cover document ingestion, chunking, embeddings, vector search, metadata filtering, reranking concepts, prompt grounding, citations and RAG evaluation.

What you will be able to do

✓Build document ingestion pipelines
✓Use embeddings and vector search
✓Improve retrieval quality
✓Ground LLM answers in source content
✓Evaluate RAG systems

Curriculum

RAG Architecture2 Weeks
Retrieval vs generation
Ingestion pipeline
Chunking strategies
Embeddings & Vector Search2 Weeks
Embedding models
Vector stores
Similarity and metadata filtering
Retrieval Quality2 Weeks
Query transformation
Reranking concepts
Hybrid retrieval concepts
Grounded Generation2 Weeks
Prompt context
Citations
Hallucination reduction
Evaluation & Capstone2 Weeks
Retrieval metrics
Answer evaluation
Knowledge assistant deployment

Projects you can build

College Knowledge Assistant

RAG assistant for policies, course information and FAQs with citations.

Technical Documentation Copilot

Search and answer from product manuals and internal documents.

Frequently asked questions

Do I need prior experience?

The entry requirement depends on the course level. Beginner courses start from fundamentals; intermediate tracks expect basic programming or domain knowledge.

Will I build projects?

Yes. Every course includes practical labs and one or more projects designed to help you demonstrate the skills you learn.

Is a certificate included?

Yes. A completion certificate can be issued after meeting attendance, assessment and project requirements.

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