Building AI-Powered Products
Learn to build, ship, and scale AI features — from your first API call to production agents. For developers who want to stop watching tutorials and start building.
LLMs Are Next-Token Predictors — Everything Follows From That
Understand the token prediction loop, estimate token costs with the ¾-word rule, and apply model routing to cut inference spend.
Prompt Design
A prompt is a specification — write it with the same discipline you apply to production code.
Context Engineering
Learn how to decide what information to put in the prompt, where to retrieve it from, and how to arrange it so the model can use it.
API Integration: Retries, Backoff, and Graceful Fallbacks
Learn how to handle timeouts, rate limits, and server errors in production AI API calls using exponential backoff and graceful fallbacks.
Evals: Measure Before You Improve
Build a three-layer eval pyramid — heuristics, LLM-as-judge, and human review — so every prompt change is an experiment with a result.
Agent Architecture
Understand how the agent loop works, how multi-agent systems decompose tasks, and why safety controls are non-negotiable in production.
Safety & Guardrails: Defense in Depth
Build layered input/output guards that keep your system safe even when the model itself is compromised.
Production AI — Latency, Cost, and Observability
Instrument your AI system for latency, cost, and quality, then use prompt caching and model routing to cut costs by 75%.
Certification Quiz
Pass with 80% or higher to earn your certificate