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Generative AI Training Program

Master the technology behind ChatGPT and beyond — LLMs, prompt engineering, RAG, fine-tuning with LoRA/QLoRA, and production-grade Generative AI applications.

Course Highlights

What You'll Master

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LLMs & Prompt Engineering

Master tokens, embeddings, sampling strategies and advanced prompting techniques.

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RAG & Vector Databases

Build retrieval-augmented systems using FAISS, Pinecone and LangChain.

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Fine-Tuning with LoRA / QLoRA

Adapt open-source LLMs efficiently, even on limited GPU memory.

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Production GenAI Applications

Deploy LLM-powered apps with FastAPI, evaluation pipelines and monitoring.

Curriculum

Detailed Modules

  • Evolution of Language Models
  • Transformer architecture (Encoder, Decoder, Self-Attention)
  • Tokens and tokenization
  • Embeddings and vector representations
  • Context window and attention mechanism
  • Pretraining vs Fine-tuning vs Prompting
  • API-based model access
  • Sampling strategies (temperature, top-p, top-k)
  • System prompts and role-based prompting
  • Structured output generation (JSON mode, schema control) & function calling
Hands-on
  • Build a structured-response chatbot
  • Zero-shot and Few-shot prompting
  • Chain-of-Thought reasoning & Self-consistency
  • Prompt evaluation techniques
  • Guardrails and response validation
Mini Project
  • AI Resume Reviewer
  • Content Generation Assistant
  • Introduction to LangChain architecture
  • Core components: LLM wrappers, Prompt templates, Chains, Memory, Tools, Agents
  • Sequential chains and conversation memory
Hands-on
  • Build a conversational assistant with memory
  • What are embeddings? Embedding generation workflow
  • Vector similarity search
  • FAISS / Pinecone / Weaviate integration; indexing and retrieval
Hands-on
  • Semantic search system
  • Vector database integration
  • RAG architecture and document loaders
  • Text chunking strategies and retriever configuration
  • Context injection and evaluation of RAG systems
Industry Project
  • Enterprise Document Q&A Assistant
  • Legal or Policy Knowledge Bot
  • Function calling mechanism; tool schemas and structured interfaces
  • LangChain Tool abstraction; ReAct (Reason + Act) pattern
  • Tool selection strategies; error handling and hallucination mitigation
Hands-on Projects
  • Research Agent using Web Search API
  • CSV Data Analysis Agent
  • Multi-tool Task Automation Agent
  • Agent architectures; planning and reasoning loops
  • Multi-agent systems (conceptual overview)
  • Agent memory and state management; observability and tracing
Mini Project
  • Autonomous research assistant
Core Limitations
  • Hallucination — fluent but factually incorrect outputs
  • Bias and fairness issues
  • Context window & knowledge cutoff limitations
  • Tool hallucination; lack of reasoning consistency
Mitigation Techniques
  • Retrieval-Augmented Generation; tool grounding
  • Guardrails, structured outputs & prompt constraints
  • Output validation layers; human-in-the-loop workflows
Evaluation Methods
  • Manual & automated evaluation
  • Ground-truth comparison; LLM-as-a-judge approach
Metrics
  • Accuracy, Precision / Recall, BLEU / ROUGE
  • Exact match score, Hallucination rate, Faithfulness score (RAG)
  • Latency and cost metrics
Agent Evaluation
  • Tool call correctness; reasoning trace validation; trajectory evaluation
Fine-Tuning Fundamentals
  • Full fine-tuning vs parameter-efficient methods (PEFT)
  • Supervised fine-tuning (SFT) and instruction tuning
  • Dataset preparation, formatting and training pipeline overview
LoRA (Low-Rank Adaptation)
  • Injects trainable low-rank matrices while freezing original weights
  • Reduces trainable parameters; efficient for large models
QLoRA (Quantized LoRA)
  • Combines 4-bit quantization with LoRA adaptation layers
  • Enables fine-tuning of very large models on limited GPU memory
Hands-on
  • Fine-tune a small open-source LLM using LoRA/QLoRA on a custom dataset
  • Multimodal LLMs
  • Function calling + fine-tuned models
  • Alignment techniques & RLHF (conceptual overview)
  • Safety alignment
  • Serving fine-tuned models; API deployment (FastAPI)
  • Monitoring, logging, cost optimization and scaling LLM applications
  • Security considerations
Capstone Project
  • Domain-specific dataset preparation; fine-tuning using LoRA/QLoRA
  • RAG integration, tool calling, evaluation metrics & deployment as API
Example Capstones
  • AI Legal Assistant with fine-tuned domain model
  • Enterprise Knowledge Assistant
  • Domain-specific Chatbot (Healthcare, Finance, Education)
  • Autonomous Research Agent
Tools Covered

Tools You'll Master

link LangChain api OpenAI / LLM APIs travel_explore FAISS / Pinecone smart_toy Hugging Face tune LoRA / QLoRA cloud FastAPI

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