All the Hard Stuff Nobody Talks About When Building Products...

All the Hard Stuff Nobody Talks About When Building Products With LLMs

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People need to be more thoughtful building products on top of LLMs. The fact that they generate text is not the point.

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When we deliver a model we make sure we don't reach X seconds of latency in our API. Before even going into performance of LLMs for classification, I can tell you that with the current available tech they are just infeasible.

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LinuxSpinach

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5h ago

^ this. And especially classification as a task, because businesses don’t want to pay llm buck... See more

r/MachineLearning - Reddit

Nicolay Gerold added

We're doing NER on hundreds of millions of documents in a specialised niche. LLMs are terrible for this. Slow, expensive and horrifyingly inaccurate. Even with agents, pydantic parsing and the like. Supervised methods are the way to go. Hell, I'd take an old school rule based approach over LLMs for this.

One thing that is still confusing to me, is that we've been building products with machine learning pretty heavily for a decade now and somehow abandoned all that we have learned about the process now that we're building "AI".

The biggest thing any ML practitioner realizes when they step out of a research setting is that for most tasks accuracy has ... See more

Ask HN: What are some actual use cases of AI Agents right now? | Hacker News

Nicolay Gerold added

You are assuming that the probability of failure is independent, which couldn't be further from the truth. If a digit recogniser can recognise one of your "hard" handwritten digits, such as a 4 or a 9, it will likely be able to recognise all of them.

The same happens with AI agents. They are not good at some tasks, but really really food at others.

A rough analogy to the current LLM process is that making a new model is like baking a cake. You figure out your data and algorithms—like mixing the batter—and then you pretrain the model, that is, run it on a large number of computers for several months—like putting it in the oven—and then at the end you do some “post training”—like frosting and d... See more

Avital Balwit • My Last Five Years of Work

Max Beauroyre added

"A key challenge of (LLMs) is that they do not come with a manual! They come with a “Twitter influencer manual” instead, where lots of people online loudly boast about the things they can do with a very low accuracy rate, which is really frustrating..."

Simon Willison, attempting to explain LLM

Johann Van Tonder added

One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up bugs... See more

Ask HN: What are some actual use cases of AI Agents right now? | Hacker News

Nicolay Gerold added

The need for better AI or LLM-specific infrastructure, along with the host of problems that come with non-deterministic of LLMs, means that there’s more software work ahead of us, not less. Abstraction layers like LLMs create more possibilities and thus, more work.

Is this a good thing or a bad thing? I’m not sure.

A great example of this is frontend... See more

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

Nicolay Gerold added

the science problem. We’ve all spent the last year talking about ‘error rates’ or ‘hallucinations’ (indeed, I also wrote about them here). The breakthrough of LLMs is to create a statistical model that can be built by machine at huge scale, instead of a deterministic model that (today) must be built by hand and doesn’t scale. This is why they work,... See more

Benedict Evans • Unbundling AI

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