Should I create custom RAG systems manually instead of using frameworks like LangChain in the current no-code AI era?

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1

I’m considering developing a custom RAG (Retrieval-Augmented Generation) system from the ground up without relying on popular frameworks like LangChain, LangGraph, or LlamaIndex. However, I’m wondering if this approach is still valuable for career development.

With the rise of no-code AI platforms and drag-and-drop tools for building AI agents, I’m questioning whether manual implementation skills are still relevant. Most companies seem to prefer using existing tools and platforms rather than building everything from scratch.

My main concerns are:

  • Will employers value custom RAG development experience?
  • Is it worth the extra time and effort when no-code solutions exist?
  • Should I focus on learning existing frameworks instead?

I want to make sure I’m investing my time in skills that will actually help me stand out in job interviews and advance my career in AI development.

2

it really depends on what kind of job you’re aiming for! smaller companies really appreciate seeing some custom stuff - it shows you’re hands-on. but if you’re hitting up big firms, knowing those frameworks like LangChain can be more valuable. so, think about your target.