AI and LLM Integration for Frontend
The patterns every FAANG team is building right now. Streaming AI responses, token-by-token rendering, conversation state management, tool calls, and AI UI architecture.
Why every major AI product streams token-by-token, how Server-Sent Events work at the protocol level, and how to build a production-grade SSE consumer with fetch and ReadableStream.
How to consume LLM responses byte by byte. ReadableStream, the reader protocol, TextDecoder's UTF-8 boundary problem, TextDecoderStream, async generators, TransformStream pipelines, and AbortController for cancellation.
Why boolean flags break AI streaming UIs. How to model streaming as a finite state machine with explicit states, valid transitions, and clean React patterns using useReducer.
Why naive re-rendering on every LLM token kills performance, and how to build buttery-smooth streaming UIs with batching, append-only updates, smart auto-scroll, and virtualized message lists.
LLMs stream markdown token by token. Learn why naive rendering breaks, what Flash of Incomplete Markdown is, and how incremental parsers like streaming-markdown solve the O(n) per-token reparse problem.
How to manage LLM conversation state in frontend apps — message arrays, token budgets, AI State vs UI State, tool call flows, thread management, and the useChat hook.
How LLMs request actions through structured tool calls, the multi-turn execution loop, streaming partial inputs, and building polished UI for tool execution states.
How to make AI chat feel instant with optimistic updates, temporary IDs, error rollback, and race condition prevention — the patterns behind every great chat UI.
How to persist AI conversations across sessions, devices, and page refreshes — from in-memory state to IndexedDB to server-side storage with pagination, sync, and export.
Force LLMs to output schema-conformant JSON every time. OpenAI strict mode, Anthropic tool_use trick, Vercel AI SDK with Zod, streaming structured data, and production error handling.