A structured path through the modern AI stack — taught in Telugu, built for shipping. Every lesson has objectives, examples, and a recap. Progress saves as you go.
A plain-language explanation of large language models and why they predict text instead of looking things up.
Tokens are the units a model reads and bills on — here is how they work and why context windows matter.
Practical prompt structure — roles, examples, and constraints — that reliably gets useful output.
Get API keys, a clean project, and safe secret handling in place before you write real code.
Send your first real requests — messages, parameters, streaming, and handling errors gracefully.
Get reliable JSON out of a model by defining a schema and validating every response.
Turn text into vectors so you can search by meaning instead of exact keywords.
Give a model facts it never trained on by retrieving relevant text and putting it in the prompt.
Let a model call your functions — fetch data, do math, take actions — instead of guessing.
Combine a model, tools, and a loop so it can plan and act across multiple steps.
Use an agentic coding tool and connect it to your own tools and data with MCP.
Measure your AI system with a test set and graders so you can improve it on purpose.
Take an AI feature from your laptop to real users — safely, observably, and within budget.