Model fine-tuning

Fine-tuning is the heavyweight option for making AI fit your event, and most organizers hear about it from vendors using it as a differentiator: "our model is fine-tuned on events data." Sometimes that's meaningful — a model tuned on real matchmaking outcomes or event-industry language can outperform a generic one on niche vocabulary and match patterns. Often it's marketing gloss over a standard model with a good prompt. The distinction matters commercially because fine-tuning is expensive to do and maintain, and the cost lands somewhere in your licence fee. For your own team's use, the honest guidance is that fine-tuning is rarely the right first move: most event use cases — copy drafting, session summaries, email responses — are solved cheaper with well-crafted prompts and retrieval over your event data. The common mistake is reaching for fine-tuning to fix a data problem; tuning a model on incomplete registration data just teaches it your gaps more fluently. One honest nuance: fine-tuned models freeze a moment in time. Your audience shifts, your categories change, and last cycle's tuning quietly ages — which is model drift, and it means fine-tuning is a maintenance commitment, not a one-off purchase.