Most AI features cost real money per call and get used twice; learn to pick the ones that pay off and ship them end to end.
Your team keeps getting asked to 'add AI', and the usual answer is a chatbot nobody uses. Meanwhile the bill arrives every month, the feature takes four seconds to respond, and one confidently wrong answer costs you a customer's trust. This course is for the people who have to make those calls: product managers and full-stack developers adding AI to an app that already exists and already has users. You start by scoring feature ideas so the bad ones die before they reach a sprint. Then you learn the building blocks in plain terms — large language model (LLM) chat calls, embeddings, vector search, tool calling, and vision or audio input. From there you ship: a working assistant with a streamed response, semantic search over your own data using retrieval-augmented generation (RAG), summaries and drafts, and personalization. The last four modules are the ones teams skip and regret — the interface patterns that earn trust, keeping cost and latency inside a budget, guarding against prompt injection and leaked personal data, and measuring whether the feature actually worked. Examples run in Python and Node.js.
Built by Lakshya Kumar
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
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Sign in to applyFinished the tasks? Take the prompt to your AI and get tested on it. We copy the prompt and open the app — just paste it in.
Chat is only one tool of five; knowing the other four is what turns an expensive prompt hack into a cheap, obvious feature.
Wire the key, write the system prompt, stream the answer, and put it behind a flag so a bad day is one toggle away from fixed.
Let users ask questions of your own docs and get cited answers — plus a way to tell when retrieval, not the model, is at fault.
A summary button is easy to add and easy to get wrong; pin down the output shape, the quality bar, and what happens when it flops.
Tailored results only count if they beat the untailored ones — measure the difference, handle new users, and don't creep anyone out.
Users forgive a slow or uncertain answer if the screen is honest about it — and abandon a feature that hides its mistakes.
Cache, route to cheaper models, and set a latency budget before the invoice or the spinner becomes the reason people churn.
Prompt injection, leaked personal data, and confident nonsense are the three ways an AI feature makes the news — close all three.
Decide up front what success looks like, instrument it, then use real usage to expand the feature, fix it, or shut it off.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Add one AI feature to a real or sample app end to end: integration, prompt design, streaming UX, guardrails, cost controls, and a defined success metric. Submit the live feature (or demo), the code, and a short writeup of the metric you'll judge it by.
Assess your product for AI opportunities using the rubric from Module 1, pick the highest-value one, and write a spec: the job-to-be-done, the primitive, the UX, cost/latency budget, trust risks, and the success metric. Submit the assessment + spec.
I'm taking "Adding AI to Your Product" — a practical course for product builders integrating AI into an existing app: spotting opportunities, shipping features, getting UX/cost/trust right, and measuring results. Python and Node examples. Here's my context: 1. My product and who uses it: [describe] 2. The AI feature I'm considering: [describe, or "not sure yet"] 3. My stack: [languages/frameworks] 4. My constraints: [latency, budget, privacy/compliance] Given that, answer: - Is this a genuinely good AI feature, or a gimmick? Use a jobs-to-be-done lens. - Which building block(s) does it need (LLM, embeddings, vector search, tools, speech/vision)? - Name 3 concrete risks (cost, latency, trust) and how I'd handle each. - What's the smallest version I could ship this week, and the one metric I'd judge it by?
Build a semantic-search / RAG feature over your product's own data: chunking, embeddings, a vector store, retrieve-then-answer with citations. Submit the working feature + a short quality evaluation.
Take an existing AI feature (yours or a sample) and redesign its UX: streaming, loading/uncertainty states, citations, editable/undoable output, and graceful failure. Submit before/after and the rationale.
Harden an AI feature for production: model routing/caching for cost, 429/backoff + fallback for reliability, and guardrails (input/output validation, PII, moderation) for safety. Submit the hardened feature + a before/after cost and a red-team check.
Messages, streaming, tool use, and prompt caching used across the course.