For product builders integrating AI into an existing app: spot the opportunities worth building, ship AI features end to end, get the UX, cost, and trust right, and measure what works.
Ten modules, ~100 challenges for PMs and full-stack developers adding AI to a real product. From 'should we even add AI here?' through the building blocks (LLMs, embeddings, vector search, tools), shipping your first assistant, semantic search over your data, generation and personalization features, AI-native UX, cost/latency/rate-limit discipline, trust & safety, and the measure-iterate loop. Python and Node examples throughout, practical and product-minded rather than infrastructure-deep.
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 applyComplete 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.