
Reasoning makes translations worse
We tend to believe that making the LLM reason more can only improve its answer, regardless of the task. However, when translating from a language to another, the opposite happens.
My personal blog. I write about AI, LLMs, Open Source and Python, with some occasional diversion.
Live Last read · last published · next check

We tend to believe that making the LLM reason more can only improve its answer, regardless of the task. However, when translating from a language to another, the opposite happens.

The intelligence of modern AI agents is due to the LLMs, but their practical capabilities only exist thanks to their harness.

If you never pinned the reasoning effort across model upgrades, you may have accidentally changed your reasoning effort dramatically.

Token prices keep falling with every new model release. So how come AI bills are only rising? Is it structural, or is there a way to address it?

We all know LLMs don’t always respond the same thing to slight changes of prompt. But why does their answer differ also when the prompt is identical? And what can we do to prevent it?

Some agentic applications don’t use vector DBs for search. Is it a good idea?

Most LLMs are hardened against classic prompt injection attacks. But AI agents also behave like naive humans sometimes...

All LLMs can keep track of a short conversation. But how do they remember things long-term?

A step-by-step guide to transform your RAG pipelines into effective AI agents.

And how can 8 or 16 of them cover all possible domain of expertise?