This talk explored how LLM responses varied based on the query language, such as English vs. Czech. We analyzed the impact of language choice on model behavior and response costs.

I taught a group of 20 learners how to implement LLMs, covering agent programming, RAG systems, and embeddings.

At PyCon Estonia, I led a workshop on structured output and other new OpenAI features. I also gave a lightning talk on using Apple Silicon chips for LLM fine-tuning with MLX.

At EuroPython, I presented a poster describing how LLM performance differs when asked in various languages. I also looked at how recently released models tokenize text differently compared to their older versions.

This talk investigated how well Large Language Models work in various languages by discussing token usage differences, cross-lingual embedding effectiveness, and reasoning skills across languages. It reviewed models from OpenAI, Google, and open-source projects. I also led a RAG workshop focusing on the use of LlamaIndex.

The presentation explored LLM efficiency across languages, focusing on token economy variations, cross-lingual embeddings' effectiveness in bridging language gaps, and comparing reasoning abilities in different languages.