Best LLM Engineering & RAG (Retrieval-Augmented Generation) Course in
Varanasi
Accelerate your engineering career with 100% placement assurance, high-tech offline laboratory training, 90+ Hours+ hours of intensive live projects, and 1-on-1 mentorship from top architects.
Book Free Demo Class
Speak directly with our senior technical career advisors in Varanasi.
4Achievers Varanasi Tech Campus
Experience focused, mentor-led offline learning equipped with state-of-the-art workstations, high-speed internet, dedicated faculty doubt counters, and air-conditioned classrooms.
LLM Engineering & RAG (Retrieval-Augmented Generation) Course Syllabus in Varanasi
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:
- โWhy RAG is Essential: Solving LLM Hallucinations, Knowledge Cutoffs & Data Privacy
- โAdvanced Document Parsing: PDF, DOCX, HTML, Tables & Images with LlamaParse & Unstructured
- โChunking Strategies: Fixed-size, Recursive Character, Semantic & Sentence-Window chunking
- โMetadata Extraction & Enrichment: Tagging timestamps, authors, sections & document IDs
- โBenchmarking 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:
- โDense vs Sparse Embeddings: OpenAI text-embedding-3, BGE-large, Cohere, BM25
- โSimilarity Metrics: Cosine Similarity, Dot Product, Euclidean Distance (L2)
- โVector Indexing Algorithms: HNSW (Hierarchical Navigable Small World), IVF, Flat
- โManaged Vector DBs: Pinecone, Qdrant & ChromaDB setup and CRUD operations
- โRelational Vector Extensions: PostgreSQL with pgvector for unified transactional + vector storage
- โPartitioning, 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:
- โHybrid Search: Combining BM25 Keyword Search with Dense Vector Search using RRF (Reciprocal Rank Fusion)
- โQuery Transformations: HyDE (Hypothetical Document Embeddings) & Multi-Query Generation
- โParent-Child Indexing: Small chunks for retrieval, large parent contexts for generation
- โSelf-Querying Retrievers: Natural language translation into vector metadata filters
- โSentence-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:
- โRe-Ranking Models: Cohere Rerank, BGE-Reranker, Cross-Encoders
- โContextual Compression: Pruning irrelevant sentences before passing to context window
- โPrompt Stuffing vs Map-Reduce vs Refine Synthesis Strategies
- โCitation & Grounding: Returning exact document page numbers and source URLs with answers
- โHandling 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:
- โRouter Query Engines: Dynamically choosing between multiple indexes, databases, and summaries
- โSub-Question Query Engines: Breaking complex user questions into multiple parallel sub-retrievals
- โGraphRAG Concepts: Integrating Knowledge Graphs (Neo4j) with Vector Embeddings
- โEntity Extraction and Relationship Mapping for Complex Domain Queries
- โAgentic 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:
- โRAG Triad: Faithfulness, Answer Relevance, Context Precision & Context Recall
- โEvaluation Frameworks: Ragas (Retrieval Augmented Generation Assessment) & TruLens
- โSynthetic Test Dataset Generation using LLMs
- โTracing & Telemetry with LangSmith, Arize Phoenix & OpenInference
- โRAG Security: Preventing Document-Based Indirect Prompt Injections & Data Leakage
Where Our Varanasi Students Get Hired
Over 500+ global enterprises and innovative unicorns hire directly from 4Achievers programs.
Frequently Asked Questions (Varanasi)
Everything you need to know about fees, batch timings, placement support, and prerequisites.
Why choose 4Achievers for LLM Engineering & RAG (Retrieval-Augmented Generation) Course in Varanasi?
4Achievers provides 100% placement assurance, 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 100% placement support provided in Varanasi?
Yes! Our dedicated Corporate Placement Cell coordinates guaranteed interview drives, resume redesign workshops, and mock technical grilling until you secure your desired job offer.