Generative AI, Large Language Models & Prompt Engineering
By the end of Level 4, participants will be able to explain Generative AI, LLMs, Transformers and foundation models; apply prompt engineering; call LLM APIs with Python; build RAG applications with vector search; and understand fine-tuning concepts.
What you'll master in Level 3
By the end of Level 4, participants will be able to explain Generative AI, LLMs, Transformers and foundation models; apply prompt engineering; call LLM APIs with Python; build RAG applications with vector search; and understand fine-tuning concepts.
Training Objectives & Core Deliverables
Explain Generative AI, LLMs, Transformers and foundation models.
Understand how LLMs process and generate text.
Apply effective prompt engineering techniques.
Use open and API-based LLMs from Python.
Understand tokens, embeddings and vector similarity.
Build a basic RAG application.
Understand fine-tuning and model customization at a conceptual level.
Identify enterprise use cases and limitations of GenAI.
Day-by-Day Training Schedule & Live Labs
Generative AI & LLM Foundations
Explore an LLM through Python and experiment with text generation.
Prompt Engineering
Build prompts for classification, summarization, extraction and content generation.
LLM Application Development
Build a Python-based AI assistant.
Embeddings, Vector Search & RAG
Build a basic document Q&A/RAG application.
GenAI Application & Customization
End-to-end enterprise document assistant.
Detailed Module-wise Curriculum
Prompt Fundamentals: Instruction, Context, Input, Expected output
Techniques: Zero-shot, One-shot, Few-shot, Role prompting, Structured formatting
Advanced Prompting: Prompt templates, Constraints, Delimiters, Controlling format, Hallucination mitigation
Enterprise Use Cases: Customer support, Summarization, Data extraction, Report generation
LLM APIs and endpoints (Open-source vs proprietary models)
Hugging Face ecosystem & Local LLMs
Calling models from Python & request/response workflow
Tuning parameters: Temperature, max tokens, top-p, system vs user instructions
Building reusable Python functions & handling errors/API keys
Introduction to LLM Frameworks: LangChain overview, chains, and components
What are embeddings? Text to numerical vectors and semantic similarity
Vector Databases: Why keyword search is insufficient, FAISS & Chroma overview
RAG Architecture: Ingestion, loading, chunking, embedding, vector storage, retrieval, context injection
RAG Limitations: Poor chunking, incorrect retrieval, missing context, hallucinations
Improving RAG: Chunk size, overlap, metadata, Top-K retrieval, prompt templates for RAG
Model Customization: Prompt Engineering vs RAG vs Fine-Tuning
Fine-tuning fundamentals, transfer learning for LLMs, LoRA, PEFT, instruction tuning
GenAI Evaluation: Relevance, faithfulness, groundedness, retrieval quality
Responsible GenAI: Hallucination, bias, privacy, prompt injection, sensitive data
Enterprise GenAI Architecture patterns
Certificate of Technical Competency — Generative AI, Large Language Models & Prompt Engineering
Complete all live hands-on labs to earn this verifiable competency badge. This credential unlocks progression to Agentic AI, AI Agents & Multi-Agent Systems.
Enroll in Generative AI, Large Language Models & Prompt Engineering
Reserve your seat for upcoming live batches, syllabus consultation & fee structure.Frequently Asked Questions
Frequently Asked Questions: Generative AI Training Plan
Find clear answers to common questions about our live interactive classes, cohort schedules, project mentoring, and career assistance.
You can choose to enroll in the complete 4-track master program (recommended for end-to-end expertise) or consult our academic team to join specific tracks matching your current skills.
Track 1: Scikit-learn, MLOps, ML Pipelines. Track 2: TensorFlow, Keras, CNN, NLP. Track 3: LangChain, Chroma/FAISS, RAG, Fine-Tuning. Track 4: LangGraph, MCP, ReAct, Multi-Agent Systems.
Yes, all classes are live, interactive online sessions with senior practitioners. Session recordings, code notebooks, datasets, and reading references are shared after each class.
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