Comment by hamelsmu | Hacker News Reader

Comment by hamelsmu | Hacker News Reader

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Hi, Hamel here. I'm one of the co-authors. I'm an independent consultant and not all clients allow me to talk about their work.

However, I have two that do, which I've discussed in the article. These are two production use cases that I have supported (which again, are explicitly mentioned in the article):

1. https://www.honeycomb.io/blog/introducing-query-assistant

2. https://www.youtube.com/watch?v=B_DMMlDuJB0

Other co-authors have worked on significant bodies of work:

Bryan Bischoff lead the creation of Magic in Hex: https://www.latent.space/p/bryan-bischof

Jason Liu created the most popular OSS libraries for structured data called instructor https://github.com/jxnl/instructor, and works with some of the leading companies in the space like Limitless and Raycast (https://jxnl.co/services/#current-and-past-clients)

Eugene Yan works with LLMs extensively at Amazon and uses that to inform his writing: https://eugeneyan.com/writing/ (However he isn't allowed to share specifics about Amazon)

I believe you might find these worth looking at.

You've linked to a query generator for a custom programming language and a 1 hour video about LLM tools. The cynic in me feels like the former could probably be done by chatgpt off the shelf.

But those do not seem to be real world business cases.

Can you expand a bit more why you think they are? We don't have hours to spend reading, and you say you've been allowed to talk about them.

So can you summarise the business benefits for us, which is what people are asking for, instead of linking to huge articles?

Sounds like something you could do with an LLM

They think they are real business use cases, because real businesses use them to solve their use cases. They know that chatgpt can't solve this off the shelf, because they tried that first and were forced to do more in order to solve their problem.

There's a summary for ya! More details in the stuff that they linked if you want to learn. Technical skills do require a significant time investment to learn, and LLM usage is no different.

> do not seem to be real world business cases

The first one is a real world product that lives in production that is user facing for a paid product.

The second video goes in depth about how a AI assistant was built for a real estate CRM company, also a paid product.

I don’t understand the assertion that it’s not “real world” or not “business”

Here are additional articles about these

https://help.rechat.com/guides/lucy

https://www.prnewswire.com/news-releases/honeycomb-launches-...

> The cynic in me feels like the former could probably be done by chatgpt off the shelf.

Hello! I'm the owner of the feature in question who experimented with chatgpt last year in the course of building the feature (and working with Hamel to improve it via fine-tuning later).

Even today, it could not work with ChatGPT. To generate valid queries, you need to know which subset of a user's dataset schema is relevant to their query, which makes it equally a retrieval problem as it does a generation problem.

Beyond that, though, the details of "what makes a good query" are quite tricky and subtle. Honeycomb as a querying tool is unique in the market because it lets you arbitrarily group and filter by any column/value in your schema without pre-indexing and without any cost w.r.t. cardinality. And so there are many cases where you can quite literally answer someone's question, but there are multitudes of ways you can be even more helpful, often by introducing a grouping that they didn't directly ask for. For example, "count my errors" is just a COUNT where the error column exists, but if you group by something like the HTTP route, the name of the operation, etc. -- or the name of a child operation and its calling HTTP route for requests -- you end up actually showing people where and how these errors come from. In my experience, the large majority of power users already do this themselves (it's how you use HNY effectively), and the large majority of new users who know little about the tool simply have no idea it's this flexible. Query Assistant helps them with that and they have a pretty good activation rate when they use it.

Unfortunately, ChatGPT and even just good old fashioned RAG is often not up to the task. That's why fine-tuning is so important for this use case.

Thanks for the reply. Huge fan of honeycomb and the feature. Spent many years in observability and built a some of the large in use log platforms. Tracing is the way of the future and hope to see you guys eat that market. I did some executive tech strategy stuff at some megacorp on observability and it’s really hard to unwedge metrics and logs but I’ve done my best when it was my focus. Good luck and thanks for all you’re doing over there.

Glad you like HNY and the feature! Here’s hoping we can help move a lot more of the world over to tracing :)

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