> You check its work and if it seems to do its job well enough most of the time, you just start to trust it.
replyI don't think you meant to say this but it feels like you're saying "vibes".
It would be better to use a scoring framework like Harbor or similar?
I don’t really have time to be assigning my LLM homework and designing a grading rubric for it. I judge its output like I judge my peers’ PRs.
replyThis seems to be the inherent trade-off with smaller models. They can be just as "bright"
as their larger contemporaries but they necessarily can't encode as much knowledge.
replyI think over time we'll realize this is the way forward, you want the smartest model with the least amount of embedded knowledge and you provide the knowledge externally or merge it into the weights for the domain you're interested in. Then you don't have to train a ginormous model every 6 months to pull in new information.
I think this is what the n-gram table in Qwen 4 (and 3.8-Flash-Next) is for. We’ll see if it comes down to the 27b variant.
replyA RAG solution would make time to first token awful.
> A RAG solution would make time to first token awful.
replyin your specific workflows, where specifically is the time to first token a dealbreaker? what's your typical target and what's a "no go"?
I tend to use LLMs conversationally, often as search-and-summarize but also as a pair designer. I find it very difficult to operate in this mode if prefill takes more than, say, a dozen seconds. And that only gets to the reasoning output; depending of effort level it can be multiple minutes to get a reply in a freshly compacted context.
reply