Generative AI Training in Pondicherry
Generative AI is changing how software is built and how businesses operate. IAMTECHNI offers practical Generative AI and LLM training in Pondicherry. Led by practising AI engineers, the program covers Retrieval-Augmented Generation (RAG), autonomous AI agents, vector databases, and API integrations using Python.
Regional Contact Details
Engineering Hub: Puducherry & Tamil Nadu, India
Hands-on Labs · Live Instructor Support
Course Curriculum & Syllabus
Explore module details and practical exercises in our main directory: View Course in Training Section.
Contact our admissions desk via WhatsApp or phone for upcoming batch schedules, duration, and fee details.
Key Capabilities & Advantages
Production RAG Architectures
Build enterprise question-answering systems over proprietary documentation with vector search.
LangChain & LangGraph Agents
Create multi-step autonomous AI agents equipped with external tools, web browsing, and code execution.
Multi-Model API Integration
Hands-on experience with OpenAI GPT-4o, Google Gemini, and local open-source models like Llama 3 via Ollama.
Practical Project Portfolio
Build live AI application projects hosted on your personal GitHub to showcase your practical capabilities.
Engineer Production AI Systems, Not Just Chatbot Prompts
Prompting alone is not enough for modern software engineering. Companies need engineers who understand how to connect Large Language Models (LLMs) with enterprise knowledge bases, APIs, and business databases.
In this course, you will write Python code to build semantic search engines, contextual document analyzers, and automated customer support agents using models from OpenAI, Anthropic, and Google Gemini.
Retrieval-Augmented Generation (RAG) & Vector Databases
Learn the architectural standard for enterprise AI: Retrieval-Augmented Generation. We dive deep into tokenization, semantic embeddings, chunking strategies, and vector indexing with Pinecone, ChromaDB, and PGvector.
You will build systems that ingest PDFs, Notion wikis, and SQL databases, generating citations and answering queries without hallucinations.
Autonomous Agents, Tool Calling & AI Guardrails
The future of AI lies in agentic workflows where models call external APIs, perform web searches, and execute multi-step tasks autonomously. You will work with LangChain, LangGraph, and CrewAI frameworks.
We also emphasize responsible AI deployment: latency reduction, token cost optimization, output validation with Pydantic, and privacy guardrails.
Delivery Lifecycle
01: LLM Foundations & Prompt Engineering
Understanding transformer architectures, attention mechanisms, token economics, few-shot prompting, and structured JSON outputs.
02: Embeddings & Vector Databases
Generating text embeddings, similarity metrics (cosine/dot product), and storing vectors in ChromaDB and Pinecone.
03: Advanced RAG & LangChain
Semantic chunking, hybrid search, re-ranking, conversation memory buffers, and evaluation metrics.
04: Autonomous Agents & Deployment
Building multi-agent systems with LangGraph, tool calling, FastAPI backend wrapping, and cloud deployment.
Regional Commitment in Pondicherry
Positioning Pondicherry students, researchers, and software engineers at the cutting edge of artificial intelligence through practical, production-focused engineering.
Frequently Asked Questions
Do I need a strong background in mathematics or machine learning?
No advanced mathematics or calculus is required. This course focuses on applied AI engineering—using Python, APIs, and modern frameworks to build practical applications using pre-trained foundation models.
What programming language is used in the course?
The entire course is taught in Python, the industry standard for AI and LLM development. Intermediate Python programming and familiarity with REST API integration is recommended.
How do I enquire about upcoming batches, duration, and fees?
Contact our admissions team via WhatsApp or call +91 93859 48951 for detailed information on upcoming batch dates, duration, mode, and fees.
Will we learn how to run local models without paying API fees?
Yes. We show you how to run open-weight models (such as Llama 3 and Mistral) locally on your own workstation using Ollama and Hugging Face.