Module 08: AI Product Engineering
Building a model that works in a notebook is 10% of the job. The other 90% is everything that happens between the model and the user - the UX decisions that determine whether your AI product earns trust or destroys it in the first session.
This module covers the full product engineering layer: how to design AI features that feel reliable, how to manage latency and failures gracefully, how to roll out new model versions safely, and how to measure whether your AI product is actually working.
What You Will Learn
Lessons at a Glance
| # | Lesson | Core Skill |
|---|---|---|
| 01 | AI Product Design Principles | Graceful degradation, trust calibration, failure-mode design |
| 02 | Streaming UX for LLMs | Token streaming, SSE, React progressive rendering |
| 03 | Handling LLM Latency | TTFT, speculative display, optimistic UI, latency SLOs |
| 04 | AI Error Handling and Fallbacks | Fallback chains, content filters, context overflow, degraded modes |
| 05 | Prompt UX Patterns | Guided prompting, templates, edit-and-retry, conversational UI |
| 06 | AI Feature Flags and Rollouts | Shadow mode, canary releases, A/B testing AI, kill switches |
| 07 | Personalisation and Memory | Memory injection, implicit/explicit prefs, privacy, forgetting |
| 08 | Measuring AI Product Quality | Override rate, task completion, NPS, model-product metric bridge |
Prerequisites
- Completed Module 01–07 or equivalent experience
- Comfortable with React/TypeScript and Python (FastAPI)
- Basic understanding of LLM APIs (Anthropic Claude, OpenAI)
Who This Is For
This module is aimed at AI engineers and full-stack engineers building AI-powered products - not ML researchers. The focus is on the product layer: what the user sees, experiences, and trusts. Every lesson includes production-ready code, concrete product recommendations, and interview preparation.