[benatkin]

> Machine learning (ML) has the potential to advance the state of the art in technical writing. No, I’m not talking about text generation models like Claude, Gemini, LLaMa, GPT, etc. The ML technology that might end up having the biggest impact on technical writing is embeddings.

This is maybe showing some age as well, or maybe not. It seems that text generation will soon be writing top tier technical docs - the research done on the problem with sycophancy will likely result something significantly better than what LLMs had before the regression to sycophancy. Either way, I take "having the biggest impact on technical writing" to mean in the near term. If having great search and organization tools (ambient findability and such) is going to steal the thunder from LLMs writing really good technical docs, it's going to need to happen fast.

[kaycebasques]

Realistically, it's probably the combination of both embeddings and text generation models. Embeddings are a crucial technology for making more progress on the intractable challenges of technical writing [1] but then text generation models are key for applying automated updates.

[1] https://technicalwriting.dev/strategy/challenges.html