2025-05-05
As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often | Max Woolf’s Blog (via). I mean, if you want to take someone working at BuzzFeed seriously.
No one in the AI field is happy about prompt engineering, especially myself. Attempts to remove the need for prompt engineering with more robust RLHF paradigms have only made it even more rewarding by allowing LLM developers to make use of better prompt adherence. True, “Prompt Engineer” as a job title turned out to be a meme but that’s mostly because prompt engineering is now an expected skill for anyone seriously using LLMs. Prompt engineering works, and part of being a professional is using what works even if it’s silly.
There’s also one use case of LLMs that doesn’t involve text generation that’s as useful in my professional work: text embeddings. [ … ] Text embeddings have done a lot at BuzzFeed from identifying similar articles to building recommendation models, but this blog post is about generative LLMs so I’ll save those use cases for another time.
Generally agrees with my experience, and also I need to switch to Polars:
For actual data science in my day-to-day work that I spend most of my time, I’ve found that code generation from LLMs is less useful.
Despite my success in using LLMs for one-off coding questions, I actually dislike using coding assistants for an unexpected reason: it’s distracting.
Yes, 100%:
Now we can talk about the elephants in the room — agents, MCP, and vibe coding — and my takes are spicy. Agents and MCP, at a high-level, are a rebranding of the Tools paradigm popularized by the ReAct paper in 2022 where LLMs can decide whether a tool is necessary to answer the user input, extract relevant metadata to pass to the tool to run, then return the results. The rapid LLM advancements in context window size and prompt adherence since then have made Agent workflows more reliable, and the standardization of MCP is an objective improvement over normal Tools that I encourage. However, they don’t open any new use cases that weren’t already available when LangChain first hit the scene a couple years ago, and now simple implementations of MCP workflows are even more complicated and confusing than it was back then. I personally have not been able to find any novel use case for Agents, not then and not now.