Build and ship real AI applications
Go from using AI to engineering it. Learn to build applications on large language models: chatbots that answer from your documents, agents that use tools, and AI features inside real products, with the testing and safety work that separates a demo from software people can rely on.
From prompts to products
Most AI tutorials stop at "call the API and print the answer." Real applications need much more: grounding answers in trusted data, handling long documents, controlling cost and speed, tracking quality, preventing misuse, and deploying something that stays up. This course covers that full path, one-to-one with a developer who builds AI features professionally.
You will work in Python with modern frameworks, but the emphasis is on understanding what is happening underneath, so your skills survive the next framework change.
Course roadmap
Python refresh for AI
Functions, classes, async, environment variables and working with JSON and APIs.
LLM APIs
Messages, system prompts, temperature, streaming, token costs and rate limits.
Prompting in code
Templates, structured output, function and tool calling.
Embeddings and vector search
How semantic search works; building an index with a vector database.
RAG: retrieval-augmented generation
Chunking, retrieval, re-ranking and citations so answers come from your sources.
Agents
Tool use, planning loops, memory and the guardrails that keep agents safe.
Evaluation
Test sets, automatic and human grading, regression checks and monitoring.
Security
Prompt injection, data leakage, access control and responsible AI practice.
Deployment
A web interface, an API backend, hosting and basic observability.
What you will build
Document Q&A assistant
Answers questions from a set of PDFs with citations, for a school, clinic or business.
Tool-using agent
An agent that queries a database and an external API to complete multi-step tasks.
AI feature in a web app
A summariser, classifier or writing assistant integrated into a full-stack app.
Generative AI development questions
How much Python do I need?
You should be comfortable with variables, loops, functions and lists. If not, start with our Python tutoring and move into this course after a few weeks.
Which models and frameworks do you use?
We work with several leading model providers and popular open-source frameworks, and also build key parts by hand so you understand them. Tools change fast; principles do not.
Will this help me get a developer job?
Deployed AI projects with clean code and documentation are strong portfolio evidence for internships and junior roles. We also review your GitHub and CV in the final module.
Is this the same as machine learning?
No. This course builds applications on top of existing large models. If you want to train models and understand the mathematics, see machine learning and data science.
Related courses and guides
Book a free AI development demo
Tell us who the classes are for and what you want to learn. A real person from our admissions team replies on WhatsApp, usually within a few hours, and books your free demo.
- Free first session, no card needed
- Skills check and a written learning plan
- Matched with a specialist tutor within 24 hours
- Prefer to talk? Message us on WhatsApp