Generative AI for Business
Ship AI features into a real product — RAG, fine-tuning, evaluation and unit economics.
What you'll learn
- Pick the right model for a job on cost, latency and quality
- Design AI features that degrade gracefully instead of embarrassing you
- Decide between prompting, RAG and fine-tuning with evidence
- Fine-tune a small model on your own data
- Build an evaluation harness before you ship
- Model the unit economics of an AI feature
- Handle privacy, data retention and customer trust
What's included
Course content 5 sections · 18 lessons · 5h 44m total
Requirements
- Basic technical literacy — you do not need to be an engineer
- An API key from any major LLM provider
- A product or idea you want to add AI to
Description
A practical course for founders, product people and engineers who need to put generative AI into a product that customers pay for.
We cover the decisions that matter: build vs buy, when fine-tuning beats prompting, how to price an AI feature when inference costs money, and how to evaluate quality before customers do it for you.
Light on hype, heavy on the trade-offs nobody mentions in demos.
Your instructor
Pratik Naiya
Pratik Naiya builds production AI systems, automations and data products at HackWithPro. He has shipped AI agents, RAG helpdesks and document-intelligence pipelines for real businesses, and teaches the exact workflow he uses on client projects.
Frequently asked questions
Still deciding?
Tell us what you are trying to build and we will say honestly whether this course is the right fit — or point you at something better.