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Frontend + AI courses

Most AI products are not Python scripts. They're web apps that stream responses, handle auth, and feel responsive. These courses focus on the frontend side of AI: Next.js for the framework, Vercel AI SDK for streaming, and the integration patterns that connect a React UI to your RAG or agent backend.

Curated by Param Harrison

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If you're a frontend engineer moving into AI work, this is the path. If you're a backend engineer who needs just enough frontend to ship a product, the courses are narrow enough to skim.

Showing 7 of 7 courses

Prompt Engineering Crash Course

The prompting techniques senior AI engineers use daily, and most tutorials skip.

NoobsFree
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AItoolsloop

Learn AI Agents from Scratch

Build AI agents with pure Python. No LangChain. No CrewAI. No magic.

BeginnerFree
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orchestrator

Advanced Design Patterns in AI Agents

Battle-tested agent patterns. The reference guide that didn't exist until now.

AdvancedFree
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Frontend Masterclass for Software Engineers

Make the modern frontend stack finally make sense. From raw DOM to a Fullstack app you actually understand.

BeginnerPro
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TEXT

Local voice transcription with Whisper and LLM post-processing

Record audio in the browser, transcribe locally with Whisper, and clean output with an LLM pipeline.

IntermediatePro
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Building RAG applications with Next.js and the Vercel AI SDK

Implement direct LLM chat, native tool RAG, and LangChain vector RAG in one Next.js app with the Vercel AI SDK.

IntermediateFree
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Full-stack agentic AI with Next.js and LangGraph

Ship a multi-step AI agent in one Next.js app with streaming tools and memory.

AdvancedPro
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Common questions

Frontend + AI: quick answers

  • Next.js or Remix?

    Next.js in these courses because that’s where the AI ecosystem has gravitated. Vercel AI SDK, server actions, streaming primitives all land there first. Remix is great; it just has fewer AI-specific libraries.

  • How do I stream tokens to the UI?

    Vercel AI SDK’s streamText on the server, useChat or useCompletion on the client. The Streaming LLM Applications course covers the full wiring, including error handling and tool streaming.

  • Do I need the Vercel AI SDK specifically?

    No, but it saves real time. Alternatives: custom SSE + EventSource, or the Anthropic/OpenAI SDKs with streaming helpers. The sdk version is just the shortest path.

  • Where should RAG live, server or client?

    Server in 95% of cases. Retrieval needs API keys, vector-store access, and logging. Client-side RAG is reserved for specific privacy use cases.

  • How does this pair with FastAPI?

    Common split: FastAPI for the AI engine (retrieval, agents, eval), Next.js for the UI. They talk via JSON or SSE. The RAG-with-Next.js course shows both sides.

Related paths

AI EngineerRAGPythonLLM Observability
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