Generative AI Foundations
Understand language models, write useful prompts and recognise where human judgement matters.
Connect a usable interface, a reliable API and a model into one working application.
Live, interactive classes with time for every learner's questions and code reviews.
Request this batch A 5–10 learners batch starts once enough learners ask. You confirm and pay only when dates are set.Developers who can build a basic web application and want to add useful AI features.
Basic JavaScript or TypeScript, HTTP and familiarity with a web framework.
Work through the modules in order. Use the supporting guide for context, then complete the practice task before moving on.
Written lessons and self-guided practice. Live sessions and mentorship are arranged separately.
Define a specific user, input and useful output. Write acceptance criteria before choosing a model. Keep the first version small enough to test with real examples.
Write a one-page specification for an assistant that solves one narrow problem.
A usable AI interface explains what to enter, what is happening and how to recover. Design idle, loading, success, empty and failure states. Avoid promising a response before the server confirms it.
Build a form and results panel with each state represented.
Validate input on the server, keep credentials server-side and set a request timeout. Return a clear response contract so the interface does not depend on raw provider errors.
Implement a POST endpoint with validation and a predictable error response.
Separate system instructions from user content. Constrain the response shape when your interface needs structured data, then validate the returned structure. Do not let generated content execute as code.
Connect the endpoint to a model and reject malformed responses before rendering them.
Test slow requests, provider errors and empty results. Log request identifiers and useful timing information without unnecessarily storing sensitive prompts.
Add timeout handling and record latency for a small set of test requests.
Check environment variables, access boundaries and the behaviour of the deployed build. Document what the feature can and cannot do. Keep a rollback path.
Deploy the application and demonstrate three happy paths and three failure paths.
Finishing Full-Stack AI Development is planned to include the support below. It opens with the first batch; enrolment pages confirm what is included before you pay.
Issued with a unique ID once your final project passes review. It records what you built, not just attendance.
Certification policyPortfolio and GitHub review, resume feedback, a mock interview and referrals when suitable openings come up. No placement guarantee.
Career mentorshipGuidance on pricing, proposals and your first client project, plus a review of your freelance profile and portfolio.
Build client-style projectsAn invite to the Innovate with Ajay community to meet builders, find co-founders, join hackathons and get feedback on product ideas.
Explore the communityShare your interest. We’ll confirm availability, batch dates and the fee separately, so you can decide when the details are ready.
Understand language models, write useful prompts and recognise where human judgement matters.
Build a document assistant that retrieves useful context and shows where an answer came from.
Connect tools and workflows with clear permissions, checkpoints and human approval.