eexpand shares AI views at Botify’s “Engineering Days”

At Botify’s Engineering Days, eexpand’s Chief Product Officer, Thaddée Reynaud, shared key strategies to optimize [LLM) Large Language Models at scale. Emphasizing specialization, input optimization, model adaptability, and ongoing evaluation, he highlighted how these pillars ensure high-quality AI outputs. As AI reshapes global trade, eexpand remains committed to advancing reliable, efficient, and intelligent trade solutions.

You can’t control the quality of LLM outputs

Because of their non-deterministic nature, LLMs don’t produce the exact same response twice. At scale, this can lead to inconsistencies, making it seem impossible to ensure outputs quality.

But is that really the case?

At eexpand we’ve spent years building AI assistants for international trade. While LLMs aren’t completely predictable, we’ve learned that the right combination of techniques, processes, and strategies can dramatically improve their quality.
Yesterday, I had the opportunity to talk about this at Botify‘s Engineering Days.💡

4 key insights I shared:
✅ 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗱𝗿𝗶𝘃𝗲𝘀 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: hyper-focused AIs tend to outperform generalist ones.
✅ 𝗕𝗮𝗱 𝗶𝗻𝗽𝘂𝘁 = 𝗯𝗮𝗱 𝗼𝘂𝘁𝗽𝘂𝘁: the right data and the right prompts will make all the difference.
✅ 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝗺𝗼𝗱𝗲𝗹: things change fast, therefore adaptation & flexibility are key.
✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆: A/B testing, feedback loop, and monitoring are non-negotiable.

A big thanks to the Botify team for the invitation and the warm welcome!
There was very insightful discussions with Botifyers, always a pleasure to exchange with AI enthusiasts in such a great environment.