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Comment by zahlman | Hacker News Reader

Many web searches that once would have been practically impossible (e.g. due to semantic overloading of the obvious search terms) have become easy, and an LLM can go above and beyond that to synthesize information from multiple sources on different topics. It's as if the power of database joins has come to web research. Taking multiple "conversation turns" to refine with an LLM chat bot is also considerably more powerful than a search engine's "AI overview".

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Are you mainly using LLMs as a search replacement or augmentation? Are you using it in other capacities like code generation, etc?

Not the person you're replying to. But I do most of my "deeper" research online with LLM backed tooling these days. Product research in particular you need to instruct the tools to reference latest data and not rely on "memory". Many brands have excellent reputations that degrade steeply over a period of months or years. I've been bit by this both with and without LLM tooling though. If I'm searching for the hours of a local restaurant I'll still just use a search engine.

Yep, that more or less matches my experience.

I will share one surprisingly poor experience I had: I asked an earlier iteration of ChatGPT to build a probability model based on the FIFA tournament pools / brackets and current bookkeepers' odds. It made a complete mess of things, not demonstrating any understanding of how qualifiers would be paired up. It offered some case analysis where the cases were poorly defined and overlapping. Plus it got stuck in that obnoxious emoji-heavy Markdown table mode you see in all those GitHub READMEs (which otherwise hasn't been an issue for me). I thought it would be good at this, being a synthesis of basic mathematical analysis (probably following standard techniques) and basic research, but no.

Both. I've developed an intuition over the years for what's easy or hard to search for, and for how much work I'd likely have to do after finding a hit. So I mentally balance that against the costs of delegating to someone else's model and evaluating the results. I've also used it for code generation, but not in anything like an "agentic workflow"; I get it to bang out an 80% solution for a new feature and bang the rest into shape myself. But it's much more enjoyable for brainstorming and refining code ideas, even if the code gen is saving me time.

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