Best LLM Engineering & RAG (Retrieval-Augmented Generation) Course in
Noida
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LLM Engineering & RAG (Retrieval-Augmented Generation) Course Syllabus in Noida
Curriculum co-designed with engineering leads from tier-1 MNCs. Click each module below to view detailed topics, live coding projects, and industry tools.
01
RAG Architecture Fundamentals & Document Parsing
14 Hours
RAG Architecture Fundamentals & Document Parsing
Understand the naive RAG failure modes and master advanced data ingestion for enterprise unstructured files.
Core Technical Topics & Competencies Covered:
- ✔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
- ✔Metadata Extraction & Enrichment: Tagging timestamps, authors, sections & document IDs
- ✔Benchmarking Chunk Sizes for Optimal Retrieval vs Context Length
02
Embeddings & Vector Database Engineering
16 Hours
Embeddings & Vector Database Engineering
Dive deep into embedding vector mathematics, high-dimensional similarity metrics, and database indexing.
Core Technical Topics & Competencies Covered:
- ✔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
- ✔Managed Vector DBs: Pinecone, Qdrant & ChromaDB setup and CRUD operations
- ✔Relational Vector Extensions: PostgreSQL with pgvector for unified transactional + vector storage
- ✔Partitioning, Sharding & Multi-Tenant Namespace Isolation
03
Advanced Retrieval Strategies & Hybrid Search
16 Hours
Advanced Retrieval Strategies & Hybrid Search
Overcome semantic blind spots with hybrid sparse-dense search, query expansions, and multi-query routing.
Core Technical Topics & Competencies Covered:
- ✔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
- ✔Self-Querying Retrievers: Natural language translation into vector metadata filters
- ✔Sentence-Window Retrieval: Fetching surrounding sentences for precise context
04
Re-Ranking, Context Compression & Synthesis
14 Hours
Re-Ranking, Context Compression & Synthesis
Refine retrieved chunks to maximize relevance and eliminate LLM 'Lost in the Middle' attention degradation.
Core Technical Topics & Competencies Covered:
- ✔Re-Ranking Models: Cohere Rerank, BGE-Reranker, Cross-Encoders
- ✔Contextual Compression: Pruning irrelevant sentences before passing to context window
- ✔Prompt Stuffing vs Map-Reduce vs Refine Synthesis Strategies
- ✔Citation & Grounding: Returning exact document page numbers and source URLs with answers
- ✔Handling Long Context LLMs: When to use RAG vs 1M+ Token Context Windows
05
Agentic & Graph-Augmented RAG (GraphRAG)
16 Hours
Agentic & Graph-Augmented RAG (GraphRAG)
Level up from single-hop retrieval to multi-hop reasoning, routing agents, and Knowledge Graph integration.
Core Technical Topics & Competencies Covered:
- ✔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
- ✔Entity Extraction and Relationship Mapping for Complex Domain Queries
- ✔Agentic RAG Workflows with LangGraph for iterative fact verification
06
RAG Evaluation, Observability & Security
14 Hours
RAG Evaluation, Observability & Security
Quantitatively evaluate RAG performance, track telemetry, and protect against prompt injection.
Core Technical Topics & Competencies Covered:
- ✔RAG Triad: Faithfulness, Answer Relevance, Context Precision & Context Recall
- ✔Evaluation Frameworks: Ragas (Retrieval Augmented Generation Assessment) & TruLens
- ✔Synthetic Test Dataset Generation using LLMs
- ✔Tracing & Telemetry with LangSmith, Arize Phoenix & OpenInference
- ✔RAG Security: Preventing Document-Based Indirect Prompt Injections & Data Leakage
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Frequently Asked Questions (Noida)
Everything you need to know about fees, batch timings, Career Guidance & Interview Prep, and prerequisites.
Why choose 4Achievers for LLM Engineering & RAG (Retrieval-Augmented Generation) Course in Noida?
4Achievers provides 1-on-1 Mock Interview SessionsDedicated Support, dedicated offline labs with high-speed development machines, real-world live projects, and personal 1-on-1 mentor guidance from working senior engineers.
What is the course duration and batch schedule?
The program spans 12 Weeks with both weekday regular batches (Mon-Fri) and weekend tracks (Sat-Sun) specifically designed for working professionals and university students.
Is there 1-on-1 Mock Interview SessionsDedicated Support provided in Noida?
Yes! Our dedicated Corporate Career Mentorship Desk coordinates guaranteed interview drives, resume redesign workshops, and mock technical grilling until you secure your desired job offer.