Generative AI & Large Language Models
New ProgramExpert Mentorship

Generative AI & Large Language Models

An intensive program covering the complete stack of GenAI, from prompt engineering to building sophisticated AI agents and RAG pipelines.

Limited Time Offer
₹12,999₹49,999
4-6 Weeks
40-50 Hours

Live Q&A

Expert doubt clearing

Career Support

1:1 Mentorship

Program Curriculum

A comprehensive, step-by-step roadmap designed to take you from fundamentals to advanced expertise.

WEEK 1

Foundations of Generative AI & LLM Concepts

  • Evolution of AI → Generative AI → Industry applications
  • Understanding LLMs: tokens, embeddings, context windows
  • Types of LLMs: base, fine-tuned, instruction models
  • Cloud vs local LLM ecosystem (OpenAI, Groq, HF, Mistral, Meta Llama)
  • Introduction to prompting and model interaction
  • Basic hands-on with text generation & simple tasks
WEEK 2

Advanced Prompt Engineering

  • Principles of high-quality prompting: clarity, constraints, roles
  • Frameworks: RICCE, CO-STAR, TARS, REACT
  • Zero-shot, one-shot, few-shot prompting
  • Reasoning prompts: Chain-of-Thought (CoT), Tree-of-Thought (ToT)
  • Prompt optimization, debugging, and evaluating outputs
  • Safety prompting & guardrails
WEEK 3

LLM Operations, Model Selection & Embedding Intelligence

  • Choosing the right model: latency, accuracy, context, cost
  • GPT vs Llama vs Mistral vs Claude — strengths & weaknesses
  • Embeddings fundamentals: vector math, cosine similarity
  • Tokenization, pricing, throughput management
  • Choosing embedding models for semantic search and RAG
  • Practical exercise: Selecting the ideal model for real-world use cases
WEEK 4

Retrieval-Augmented Generation (RAG) Deep Dive

  • Why RAG? When to use RAG vs fine-tuning
  • Vector databases: Pinecone, Chroma, FAISS
  • Chunking strategies: fixed, recursive, semantic, hybrid
  • Indexing pipelines, metadata storage, retriever strategies
  • RAG architecture patterns: basic, advanced, and hybrid
  • Building a minimal RAG pipeline (document → embedding → retrieve → generate)
WEEK 5

Content Chains, Workflow Automation & AI Agents

  • What are chains? LLMChain, SequentialChain, RouterChain
  • Multi-step AI workflows with LangChain / LlamaIndex
  • Using tools, agents, function-calling and deterministic control
  • Document loaders, parsers, and transformation pipelines
  • Adding memory and context persistence to AI apps
  • Building content generation pipelines (summaries, insights, classification, FAQs)
WEEK 6

Evaluating, Improving & Scaling GenAI Systems

  • Prompt versioning & experiment tracking
  • RAG evaluation frameworks (RAGAS basics)
  • Latency, cost, quality trade-offs
  • Error handling, retries, fallback prompts
  • Measuring semantic accuracy & hallucination control
  • Designing AI systems for reliability
WEEK 7 & 8

Capstone Project Development

  • Accept PDFs / text documents
  • Chunk, embed, index them
  • Retrieve relevant sections using vector search
  • Generate contextual answers using LLMs
  • Provide structured outputs: summaries, insights, FAQs
  • Add memory + content chains

Ready to level up your career with Generative AI & Large Language Models?

₹12,999₹49,999SPECIAL OFFER
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