The Complete GenAI Engineer Bootcamp

Build the exact Python skills needed for Generative AI development — from environment setup and data structures to APIs, OOP, and interactive Streamlit apps. No prior coding experience required.

Environment & Python Basics:

Set up a professional Python workspace with virtual environments, dependency management, and secure API key handling using .env files.

Data Structures for AI:

Master lists, dictionaries, and JSON — the core data formats used in every LLM API response and AI data pipeline.

Functions & Control Flow:

Write clean, reusable code using functions, loops, decorators, and generators — essential patterns for LangChain tools and FastAPI endpoints.

Object-Oriented Programming:

Understand OOP principles to work confidently with LangChain's class-based architecture and build your own custom AI tool classes.

APIs & Streamlit:

Make real API calls, handle async operations, and build interactive chat interfaces using Streamlit — deployable to Streamlit Cloud.

By completing this module, you'll write professional Python code ready for GenAI development and build interactive Streamlit apps for rapid AI prototyping.

Learn the SQL skills essential for building AI applications — querying databases, working with joins and aggregations, and connecting Python to SQL backends powering AI agents and LLM dashboards.

SQL Foundations:

Understand relational databases and write SELECT, WHERE, and ORDER BY queries to retrieve and filter data from tables.

Aggregation & Grouping:

Summarise data with aggregate functions like COUNT and AVG, and group results for reporting and analytics in AI-powered dashboards.

Joins & Relationships:

Combine data from multiple tables using INNER and LEFT JOINs, and understand primary/foreign key relationships for relational database design.

Advanced SQL for AI:

Write complex queries using subqueries, CTEs, and CASE statements — the same patterns used inside LangChain SQL agents.

Python + SQL Integration:

Connect Python to SQLite and MySQL, execute queries programmatically, and use SQLAlchemy ORM — the foundation of LangChain's SQL agent.

By completing this module, you'll confidently write SQL queries for AI agents and connect Python to SQL databases — building the foundation for the SQL Agent in Module 6.

Master the art and science of communicating with LLMs to get precise, reliable outputs. Go beyond basic prompting with the same advanced techniques used by AI engineers at OpenAI, Anthropic, and Google.

LLM Fundamentals:

Understand how large language models are trained, how tokens and context windows work, and what controls model output behaviour.

Core Prompting Techniques:

Go from basic zero-shot prompts to structured few-shot examples, role prompting, and negative prompts that constrain model behaviour.

Advanced Prompting Frameworks:

Apply Chain-of-Thought, Tree-of-Thought, and ReAct — the frameworks powering modern AI agent reasoning and multi-step problem solving.

Structured Output Prompting:

Get LLMs to return JSON and Pydantic-validated data — the foundation for building reliable, production-grade AI pipelines.

Prompt Optimization:

Evaluate, iterate, and improve prompts using LLM-as-judge techniques while reducing costs, hallucinations, and injection vulnerabilities.

By completing this module, you'll apply advanced prompting techniques including CoT, ReAct, and structured outputs — and evaluate prompts systematically to build reliable AI systems.

Master LangChain — the most widely used framework for building LLM-powered applications. From your first LLM call to multi-step chains, memory-powered chatbots, and production-ready document processing pipelines.

LLM Providers & Setup:

Connect to OpenAI, Anthropic, Google, and Groq in a unified LangChain interface — and compare models for different use cases.

Prompt Templates & LCEL:

Build reusable prompts and chain components elegantly with LCEL's pipe operator — including parallel and conditional chain routing.

Output Parsers:

Reliably extract strings, JSON, and Pydantic-validated objects from LLM responses — with automatic retry on invalid outputs.

Memory & Conversational State:

Give chatbots a persistent memory using buffer, window, and summary strategies — stored in files, Redis, or databases.

Document Processing:

Load PDFs, CSVs, and web pages, split them into chunks, generate embeddings, and monitor chains end-to-end with LangSmith.

By completing this module, you'll build production-quality LLM chains using LCEL, create memory-powered chatbots, and process documents of any type for downstream AI tasks.

RAG (Retrieval-Augmented Generation) is the backbone of most enterprise GenAI applications. Build RAG systems from scratch and master the advanced patterns used in production — giving LLMs access to your private data.

Embeddings & Vector Databases:

Understand how text is converted to vectors, and work with ChromaDB, FAISS, and Pinecone for local and production-scale vector search.

Basic RAG Pipeline:

Build a complete document-to-answer pipeline: load, chunk, embed, store, retrieve, and generate — with metadata and score filtering.

Advanced RAG Patterns:

Go beyond basic RAG with hybrid search, multi-query retrieval, cross-encoder reranking, and HyDE for dramatically better retrieval accuracy.

Multi-Document & Conversational RAG:

Build RAG systems that handle PDFs, DOCX, and web sources together — with source citations and follow-up question handling.

By completing this module, you'll build complete RAG pipelines, implement advanced retrieval patterns, and create conversational chatbots that remember context and cite their sources.

AI Agents are the next evolution of LLM applications — they reason, plan, use tools, and take multi-step actions. Build real agents that browse the web, query databases, send emails, and interact with external APIs.

Agent Fundamentals:

Understand the agent loop — Observe, Think, Act — and when to use agents over chains, including their cost and reliability trade-offs.

Function Calling & Tool Creation:

Define custom tools using the @tool decorator and Pydantic schemas — giving agents access to any API or service you choose.

Built-in & Community Tools:

Equip agents with web search, code execution, file system access, and email tools from LangChain's growing tool ecosystem.

SQL & Data Agents:

Let agents query databases using plain English — the SQL agent translates natural language to SQL and executes it safely.

Multi-Tool Agents & Memory:

Build agents that intelligently select from 5+ tools, remember past conversations, and run multiple tools simultaneously.

By completing this module, you'll build AI agents that use tools for real-world multi-step tasks, create custom tools for any API, and build a SQL agent that queries databases with natural language.

LangGraph enables you to build stateful, multi-actor AI applications as graphs. Build complex agentic systems with multi-agent pipelines, human-in-the-loop workflows, and self-correcting AI systems.

LangGraph Fundamentals:

Model workflows as graphs where nodes are actions and edges are transitions — enabling loops, branching, and shared state between steps.

Building Agents with LangGraph:

Implement the ReAct agent loop as a LangGraph graph — a more powerful and controllable alternative to LangChain's AgentExecutor.

Persistence & Checkpointing:

Save and restore graph state across sessions using MemorySaver and SqliteSaver — enabling time travel and multi-threaded conversations.

Human-in-the-Loop (HITL):

Pause workflows mid-execution for human review, approval, or correction — essential for safe deployment of autonomous AI systems.

Multi-Agent Architectures:

Build supervisor agents that orchestrate specialist sub-agents in parallel — including self-correcting and plan-and-execute patterns.

By completing this module, you'll build complex stateful workflows, implement human-in-the-loop patterns, and design multi-agent systems with self-correcting capabilities.

The Model Context Protocol (MCP) is Anthropic's open standard for connecting AI models to external tools and data sources. Build custom MCP servers and integrate them with LangChain, LangGraph, and Claude Desktop.

MCP Architecture & Concepts:

Understand the MCP host-client-server model and its three primitives — Resources, Tools, and Prompts — over stdio and SSE transports.

MCP Ecosystem:

Connect Claude Desktop to official MCP servers for Filesystem, GitHub, Slack, and Google Drive — configured in a single JSON file.

Building Custom MCP Servers:

Use the MCP Python SDK and FastMCP to expose your own tools, resources, and prompt templates to any MCP-compatible AI client.

Real-World MCP Projects:

Build database, file management, and web search MCP servers — wrapping business logic as tools any AI can call.

MCP + LangChain & LangGraph:

Load MCP tools directly into LangChain agents and LangGraph nodes using langchain-mcp-adapters — including remote SSE servers.

By completing this module, you'll build production-ready custom MCP servers and connect them to Claude Desktop, LangChain agents, and LangGraph workflows.

n8n is an open-source workflow automation platform that connects AI with the real world — Gmail, Slack, Notion, Google Sheets, CRMs, and hundreds more. Build powerful AI automation pipelines that save hours of manual work every week.

n8n Fundamentals:

Install n8n locally with Docker or on n8n Cloud, and understand triggers, action nodes, and how JSON data flows between workflow steps.

Core n8n Concepts:

Control workflow logic with IF/Switch nodes, loop over data arrays, write custom JavaScript or Python in the Code node, and handle errors gracefully.

AI Nodes in n8n:

Use n8n's built-in AI Agent node with OpenAI, Claude, and Gemini models — enhanced with vector stores, document loaders, and memory.

Real-World AI Automations:

Build complete automation workflows that classify emails, qualify leads, process documents, and respond to Slack messages — all using AI.

n8n + External AI Integrations:

Call your custom LangChain APIs and LangGraph workflows from n8n, and receive events from Stripe, GitHub, and Typeform via webhooks.

By completing this module, you'll build visual AI automation workflows connecting 20+ services and automate real business processes — email, CRM, support, and social media — using AI.

FastAPI is the gold standard for deploying AI applications as production REST APIs. Wrap your LangChain apps, RAG systems, and LangGraph workflows as professional APIs — ready for frontend teams, mobile apps, and other services.

FastAPI Fundamentals:

Create GET and POST routes with Pydantic models, auto-generated Swagger docs, and proper HTTP error responses — production-ready from day one.

Async FastAPI for AI:

Use async/await to handle concurrent LLM API calls without blocking — and run background tasks after sending responses to clients.

Streaming Responses:

Stream LLM tokens to the client in real time using Server-Sent Events and WebSockets — the same experience as ChatGPT.

Auth & Session Management:

Protect your AI APIs with API key and JWT authentication, manage multi-user sessions with Redis, and configure CORS for frontend apps.

LangChain + LangGraph in FastAPI:

Expose chatbot, RAG query, AI agent, and LangGraph workflow endpoints — all with streaming, session management, and rate limiting.

By completing this module, you'll deploy production REST APIs with streaming, authentication, and session management — and test and document them professionally with Pytest and Postman.

Master AWS Bedrock — Amazon's fully managed GenAI service — and AWS Strands Agents, Amazon's newest open-source agent SDK released in 2025. Deploy AI applications serverlessly on Lambda and learn cloud-scale AI engineering.

AWS Foundations for AI:

Set up IAM roles, configure the AWS CLI, and use Boto3 and S3 to store documents and model artifacts for AI workflows.

AWS Bedrock:

Invoke Claude 3.7, LLaMA 3, and Titan models through the Bedrock InvokeModel and Converse APIs — with streaming and LangChain integration.

Bedrock Knowledge Bases (Managed RAG):

Build a fully managed RAG system on AWS using S3 as the data source, with zero infrastructure setup and the RetrieveAndGenerate API.

AWS Strands Agents SDK (2025):

Use Amazon's brand-new open-source agent framework to build tool-using agents backed by Bedrock models — and orchestrate multi-agent workflows.

Serverless Deployment:

Deploy AI APIs as AWS Lambda functions exposed through API Gateway — with Lambda Layers, cold start mitigation, and Secrets Manager for API keys.

By completing this module, you'll invoke foundation models on AWS Bedrock, build managed RAG systems, create AI agents with the Strands SDK, and deploy them as serverless Lambda functions.

Bring everything together into production-grade, portfolio-worthy projects. Learn Docker for containerisation, CI/CD for automated deployment, EC2 for hosting, and monitoring practices for AI applications running at scale.

Docker for AI Applications:

Containerise your FastAPI + Redis + ChromaDB stack with Dockerfiles and docker-compose — then push images to DockerHub and AWS ECR.

Production Deployment on EC2:

Launch an EC2 instance, serve your FastAPI app behind Nginx, configure HTTPS with Let's Encrypt, and keep it alive with systemd.

CI/CD for AI Applications:

Automate testing and deployment with GitHub Actions — every push to main triggers pytest, builds a Docker image, and deploys to your server.

Monitoring & Observability:

Trace every LLM call and agent run in production with LangSmith, monitor infrastructure with AWS CloudWatch, and track API spending.

Capstone Project Options:

Choose one of three production-grade capstone projects that combine the full tech stack — deployable on AWS with Docker and CI/CD.

By completing this module, you'll deploy a full-stack GenAI application on AWS with Docker, CI/CD, and production monitoring — a project ready to showcase in interviews and on GitHub.