Learn to build intelligent systems.

Week-by-week plan

1. Week 1Python essentials for AI work
2. Week 2Working with APIs, JSON, and environment setup
3. Week 3How LLMs work — tokens, context, and limits
4. Week 4Prompt engineering patterns that work in practice
5. Week 5Embeddings and semantic search
6. Week 6Building an AI-powered backend with FastAPI
7. Week 7A simple chat UI, connected end to end
8. Week 8Project demo, review, and next steps

What you'll build

A working AI assistant app with a FastAPI backend and a simple chat interface.

You'll be able to

• Use LLM APIs confidently from Python
• Write prompts that give reliable results
• Ship a small, complete AI application

Who it's for

Students, freshers, and developers new to AI.

Before you join

• Basic programming in any language
• A laptop with internet

• Python for AI
• APIs & JSON
• LLM basics
• Prompt engineering
• Embeddings

Week-by-week plan

1. Week 1RAG architecture — when to use it, and when not to
2. Week 2Document ingestion, parsing, and chunking strategies
3. Week 3Embeddings and vector databases (Qdrant)
4. Week 4Retrieval quality — hybrid search and re-ranking
5. Week 5Answer generation with citations using LangChain
6. Week 6Evaluating RAG — accuracy, faithfulness, and test sets
7. Week 7Auth, chat history, and deploying with Docker
8. Week 8Project demo and production review

What you'll build

A document Q&A platform with cited answers, chat history, and authentication — deployed with Docker.

You'll be able to

• Design a RAG pipeline end to end
• Measure and improve answer quality
• Deploy a RAG system people can actually use

Who it's for

Developers who know Python and want to build real knowledge assistants.

Before you join

• Comfortable with Python
• Basic understanding of APIs
• AI Engineering Foundations, or similar experience

• Chunking
• Vector databases
• Retrieval
• Citations
• Evaluation

Week-by-week plan

1. Week 1What agents are — and when a simple workflow is better
2. Week 2Tool calling and structured outputs
3. Week 3Planning, multi-step reasoning, and agent loops
4. Week 4Memory, state, and context management
5. Week 5Multi-agent patterns and orchestration
6. Week 6Guardrails, permissions, and human-in-the-loop
7. Week 7Email, voice, and workflow automation use cases
8. Week 8Project demo and reliability review

What you'll build

A workflow agent that reads requests, calls tools, and completes tasks with human approval for risky steps.

You'll be able to

• Build agents that use tools safely
• Choose between agents and simple automation
• Add guardrails and monitoring from day one

Who it's for

Backend developers and engineers automating business processes.

Before you join

• Comfortable with Python
• Experience calling LLM APIs

• Tool calling
• Agent workflows
• Memory
• Guardrails
• Automation

Week-by-week plan

1. Week 1AI system design — from business problem to architecture
2. Week 2Choosing the right intelligence: rules, ML, LLMs, RAG, or agents
3. Week 3Data pipelines and feature stores
4. Week 4Model serving and API design
5. Week 5Containers, Kubernetes, and infrastructure as code
6. Week 6CI/CD for AI — testing models and prompts
7. Week 7Evaluation and monitoring in production
8. Week 8Security, privacy, and compliance
9. Week 9Scaling, latency, and cost optimisation
10. Week 10Architecture review of your capstone design

What you'll build

A complete architecture and deployment plan for a production AI system, reviewed live.

You'll be able to

• Make sound AI architecture decisions
• Deploy and monitor AI systems at scale
• Control cost and reliability in production

Who it's for

Senior developers, tech leads, and architects building AI in production.

Before you join

• 2+ years of software development
• Experience building at least one AI or ML feature

• System design
• MLOps
• Docker & Kubernetes
• Observability
• Scaling & cost