Building Core AI Features

Most AI products aren't built around one big model call. They're built from a handful of everyday features that show up in almost every modern app: a chat assistant, a summary at the top of a long thread, fields pulled out of a messy email, a label that routes a ticket, a rewrite button, an autocomplete suggestion, a search box that understands plain English. Each looks simple in a demo and fails in its own particular way once real users and real data arrive.

This module builds those features properly, inside a realistic Next.js and Postgres app. You'll start with a streaming chat, from the route handler to the React client to conversation storage, and then follow a token from the model all the way to the screen, covering server-sent events, backpressure, cancellation, and errors that arrive halfway through a response.

From there you'll build summaries designed for a specific reader and job, extraction that turns unstructured text into typed records with a review path for uncertain cases, classification with fixed taxonomies and the metrics that tell you it works, transformation features like tone rewrites and translation, and inline writing assistance with diffs users can accept or reject piece by piece. You'll also translate natural-language questions into validated filters or read-only SQL with guardrails against bad and malicious queries.

The module closes with the architecture questions every team hits: where AI calls belong in a modern React app (route handlers, server actions, server components), how to handle keys, timeouts, and caching, and how to move slow or high-volume work into background jobs with retries, idempotency, and progress reporting. Every lesson uses TypeScript, real code you can run, and the failure modes that matter in production.