51–60 of 76 posts
Can you talk about your detection rates? False positives and false negatives. Perhaps you are still figuring this out
I’m not sure why so many folks are being so derisive on this post.
"accross" is misspelled on your front page
However, the more interesting part is the storage: Imagine ingesting 100pg PDFs or 1M tweets, and doing many/big LLM map/reduce with big (128K+) context. In observability land, we generally have small payloads, sample data, and retire data... and backends + pricing assumes that. In LLMs, we instead might want some hot, rest in the DWH, and store everything.
How have folks been dealing with these kind of mismatches? Eg, Clickhouse backends for otel? Something else? Small stuff in otel and big stuff manually in a doc store / s3 json / parquet?
Earlier quoted context omitted.
?? but who is monitoring the AI layer monitoring the AI who produced the original output ?? openai audited by claudeai which is then audited by gemini ai... then to close the loop, gemini ai is then audited by openai
I had read the OP's comment as sarcastic, but you never know these days lol Your concern would be exactly mine as well, and why I assumed "brilliant" was sarcasm, cause it feels like handing over the problem to the same solution that got you the problem in the first place?
Make it make sense..
Congratulations on launch. This is a crowded market, and there are many tools doing the same thing. How are you differentiating yourself from other tools like: Langfuse Portkey Keywords ai Promptfoo
not to mention langsmith? braintrust? humanloop? does that count? not sure what else - lets crowdsource a list here so that people can find them in future
I dont know if they are just casually using logos on their homepage but why the heck would Google (and even IBM) be using a product like this? Like… your entire future depends on getting this right and youre using a startup with 2-5 people to do this for you?!! Make it make sense..
Though I think it’s more likely there’s some Googler who happen to use this service, note how the wording is “Engineers […] use our products[…]” rather than “Companies”.
This is poorly worded. Detecting "hallucinations" as the term is commonly used, as in a model making up answers not actually in its source text, or answers that are generally untrue, is fundamentally impossible. Verifying the truth of a statement requires empirical investigation. It isn't a feature of language itself. This is just the basic analytic/synthetic distinction identified by Kant centuries ago. It's why we…