Generative AI Training in Chennai

• VS Code, Python and the essential extensions for AI development
• Virtual environments with venv and Conda, so projects stay isolated
• API keys, .env files and python-dotenv for safer development
• Installing Ollama and running an open-source LLM locally
• git init, commit, push and .gitignore from the beginning

Beginner A secure AI developer starter repo that becomes the foundation for every later project.

• Python variables, functions, lists, dictionaries and data structures
• Modules, packages, classes and object-oriented programming for AI projects
• Working with JSON, files and external APIs
• Exceptions, logging and error handling for AI applications
• Async programming for handling multiple AI and API requests

Beginner An AI-ready Python application with API integration, error handling and reusable code.

• Generative AI, machine learning, deep learning and NLP, where each fits
• Tokens, context windows, embeddings and model parameters
• Transformer architecture and attention explained practically
• Working with CSV, TXT, PDF and JSON data
• Pydantic schemas for validating AI-generated structured output

Beginner → Intermediate A structured AI data extraction pipeline that turns messy documents into validated information.

• System, user and assistant instructions and how they affect model behaviour
• Zero-shot, few-shot and role-based prompting
• Prompt templates, variables and reusable AI workflows
• Controlling tone, format, context and structured responses
• Prompt testing, iteration and reducing inconsistent outputs

Intermediate An AI content and data extraction assistant using reusable prompt workflows.

• Working with modern LLM APIs and streaming responses
• Conversation history, context management and memory
• Function calling and connecting models to external tools
• Building tool-using AI workflows with APIs and services
• Agent loops, decision-making and multi-step task execution

Intermediate An AI task assistant that combines an LLM with tools, APIs and multi-step workflows.

• Embeddings, semantic similarity and how vector search works
• Document loading, cleaning, chunking and metadata strategies
• Vector databases and similarity search
• Retrieval-Augmented Generation and context injection
• Hybrid retrieval, reranking and improving retrieval quality

Advanced An intelligent document Q&A system using embeddings, vector search and production-style RAG.

• Choosing between prompting, RAG and fine-tuning for different use cases
• Preparing high-quality datasets for model customization
• Fine-tuning concepts, LoRA and parameter-efficient approaches
• Evaluating accuracy, relevance, faithfulness and hallucinations
• Building test datasets and comparing model versions

Advanced A domain-specific AI assistant with an evaluation workflow for measuring response quality.

• Designing production-ready GenAI application architecture
• FastAPI for serving AI applications through APIs
• Docker and deployment fundamentals for AI services
• Logging, monitoring and tracking AI application performance
• API security, data privacy, guardrails and responsible AI practices
• Cost, latency and model-selection optimization

Advanced A production-ready GenAI API with deployment, monitoring and security controls.

• Define a real-world business problem and design the AI solution architecture
• Combine LLMs, RAG, vector databases, tools and agents
• Build the backend API and connect the complete AI workflow
• Test prompts, retrieval and model responses before deployment
• Deploy the application and add monitoring, security and documentation
• Prepare a GitHub portfolio, technical documentation and project presentation

Advanced Enterprise GenAI Copilot combining LLM + RAG + vector database + tools + agents + API + deployment.