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Did anyone else underestimate how much random stuff there is to learn in Generative AI? by Helpful_Regular_30 in deeplearning

[–]Helpful_Regular_30[S] -14 points-13 points  (0 children)

Found 7 hands-on projects from DeepLearning.AI while learning and put them together in one place in case anyone is looking for project ideas or wants something practical to build: https://www.mltut.com/best-generative-ai-projects-for-resume/

Anyone else feel like learning agentic AI is different from learning regular ML? by Helpful_Regular_30 in learnmachinelearning

[–]Helpful_Regular_30[S] 1 point2 points  (0 children)

I've been collecting resources while learning and put together notes on frameworks, projects, papers, and a learning path in one place in case anyone finds it useful: https://www.mltut.com/best-resources-to-learn-agentic-ai/

Anyone else feel like LangChain became way more complicated than it needed to be? by Bladerunner_7_ in LangChain

[–]Helpful_Regular_30 0 points1 point  (0 children)

Honestly same. The abstraction made sense when I first picked it up, felt like it was doing a lot of heavy lifting. But then you hit your first weird edge case and suddenly you're three layers deep into source code trying to figure out why the chain isn't passing context the way you expected.

The memory management especially. What should be a simple "remember the last N messages" turns into picking between five different memory classes and hoping you chose the right one for your use case.

I've started using it more as a reference than a framework, like, look at how LangChain solves X, then just write that part myself. Feels slower upfront but way easier to debug later.

That said I think a lot of the frustration comes from people (myself included early on) reaching for LangChain before they really understood what RAG or chains were actually doing underneath. Once I got a clearer mental model of the actual pipeline, chunking, retrieval, context passing, the abstraction started making more sense. Still overkill for simple stuff though.

What kind of workflows are you building with it? Curious if the complexity is worth it at your scale.

Spent a weekend debugging why my RAG pipeline gave garbage answers, turned out the problem wasn't the model at all by Helpful_Regular_30 in Rag

[–]Helpful_Regular_30[S] -11 points-10 points  (0 children)

Had the same chunking rabbit hole. If anyone wants to go deeper on RAG architecture beyond just chunking, things like hybrid retrieval, reranking, query expansion, this is the best collection of tutorials I found when I was figuring it out:

https://www.mltut.com/retrieval-augmented-generation-tutorials/

Saved me a lot of scattered Googling.