LLM Engineering & RAG
Master Advanced RAG Pipelines, Vector Databases & Enterprise Search.
Get Placed in Top MNCs.
Master LLM Engineering & RAG with LlamaIndex, LangChain, Vector Databases (Pinecone, Qdrant, Chroma), Hybrid Search, Re-Ranking, and RAG evaluation. 100% placement assurance.
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(10+ Years Exp)
Capstone Projects
Mock Technical Interviews
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Placement Drives
Complete LLM Engineering & RAG Curriculum — Foundations to Enterprise Scale
Engineered in collaboration with principal engineers from leading product and Fortune 500 AI teams.
RAG Architecture Fundamentals & Document Parsing
- Why RAG is Essential: Solving LLM Hallucinations, Knowledge Cutoffs & Data Privacy
- Advanced Document Parsing: PDF, DOCX, HTML, Tables & Images with LlamaParse & Unstructured
- Chunking Strategies: Fixed-size, Recursive Character, Semantic & Sentence-Window chunking
Embeddings & Vector Database Engineering
- Dense vs Sparse Embeddings: OpenAI text-embedding-3, BGE-large, Cohere, BM25
- Similarity Metrics: Cosine Similarity, Dot Product, Euclidean Distance (L2)
- Vector Indexing Algorithms: HNSW (Hierarchical Navigable Small World), IVF, Flat
Advanced Retrieval Strategies & Hybrid Search
- Hybrid Search: Combining BM25 Keyword Search with Dense Vector Search using RRF (Reciprocal Rank Fusion)
- Query Transformations: HyDE (Hypothetical Document Embeddings) & Multi-Query Generation
- Parent-Child Indexing: Small chunks for retrieval, large parent contexts for generation
Agentic & Graph-Augmented RAG (GraphRAG)
- Router Query Engines: Dynamically choosing between multiple indexes, databases, and summaries
- Sub-Question Query Engines: Breaking complex user questions into multiple parallel sub-retrievals
- GraphRAG Concepts: Integrating Knowledge Graphs (Neo4j) with Vector Embeddings
Detailed Module-by-Module Breakdown
Click each module below to explore technical topics, coding labs, and tools covered.
Tools & Frameworks You Will Master
Gain hands-on proficiency in the exact modern tech stack used across Fortune 500 tech teams.
Build Experience That Gets You Interview-Ready
Real project work. Agile exposure. Mentor feedback. A portfolio you can talk about in interviews.
Project Reviews
Prep
Already trained. Now build proof of your skills.
Turn learning into practical experience you can discuss with confidence.
- Work on live enterprise projects
- Build a real-world portfolio
- Use Agile workflows & Jira
- Get mentor feedback
- Practice with mock interviews
Career & Salary Calculator
Explore verified 2026 compensation benchmarks and market demand curves across India's top tech hubs.
Based on verified 2026 hiring data from Fortune 500 and Top MNC tech recruiters.
Frequently Asked Questions
Everything you need to know about the LLM Engineering & RAG (Retrieval-Augmented Generation) Course, batches, prerequisites & placement assurance.
Why is RAG currently one of the highest paid skills in AI engineering?
Enterprises have massive troves of proprietary documents (contracts, manuals, customer databases) that public LLMs cannot access. Fine-tuning is expensive, slow to update, and prone to hallucinations. RAG allows companies to ground LLMs in their exact private data in real time, making RAG engineers indispensable across MNCs and startups.
What is the difference between LlamaIndex and LangChain for RAG?
LangChain is a general-purpose orchestration framework for building chains and agents. LlamaIndex is purpose-built and hyper-specialized for data ingestion, indexing, and advanced RAG retrieval patterns. In this course, you will learn to use both frameworks together for production architectures.
Will we learn Hybrid Search and Re-Ranking?
Yes! Naive vector search misses exact keyword matches (like part numbers, names, codes). You will build production-grade Hybrid Search combining BM25 keyword matching with dense vectors and Cohere Re-Ranking.