Full-Stack AI Application Developer

Covers foundations of modern AI applications: product architecture (chat, files, tools, memory, evals), provider abstraction (Claude, OpenAI-compatible APIs, local models) and streaming UX with the AI response lifecycle.

Product architecture: structuring chat, file handling, tool integration, memory and evals to design coherent AI products.

Provider abstraction: principles for supporting multiple backends (Claude, compatible APIs, local models) and fallback strategies.

Streaming UX and response lifecycle: design UX around streaming, response states and the data model for users, conversations and artifacts.

Details building AI backends with FastAPI project structure, async inference endpoints and streaming, auth, rate limits, input validation and audit logs, plus file uploads, background jobs, queues and storage.

FastAPI project structure: routers, schemas and dependency injection to build maintainable backends.

Async inference endpoints: streaming responses, request cancellation and key concepts for real-time integration.

Security and operations: auth, rate limits, input validation, audit logs, and handling file uploads, background jobs, queues and storage.

Designing frontend UX for AI applications using Next.js App Router: handling chat states, prompt/file/settings forms, and accessible responsive behavior. Practical focus on integrating UX patterns specific to AI workflows.

Covers using Next.js App Router to structure full‑stack AI apps and core frontend architecture concepts.

Describes chat UI states (loading, streaming, tool calls, citations, errors) and their UX implications.

Covers building forms for prompts, files, language and model selection, plus accessibility and responsive behavior tailored to AI workflows.

Testing and deploying AI APIs and apps: unit and contract tests for AI endpoints, mocking providers and replaying conversations, deploying to cloud/serverless/container targets, and handling environment variables, secrets and release checklist.

Outlines best practices for writing unit and API contract tests for endpoints tied to AI models.

Explains mocking providers and replaying conversations to enable reproducible tests and debugging without live external APIs.

Describes deployment options (cloud, serverless, containers) and managing environment variables, secrets and a release checklist.