Fine-tuning a model involves gathering examples of desired outputs for specific tasks and performing additional training on a base model to specialize it for those tasks.

Imbuing models with a sense of taste and setting up effective feedback loops is an ongoing challenge in AI development.

Solutions may change over time due to evolving data and model quality, necessitating ongoing iteration and adaptation.

Developers can fine-tune models using a variety of data sources, such as company chat logs or marketing communications, to adjust tone and style for their specific needs. Fine-tuning a large language model involves capturing feedback data from production usage, which helps improve the model based on real customer interactions.

Fine-tuning AI models on new types of data is crucial to maintain accuracy, especially as trends and terminology evolve rapidly.

The AI tools we build need to continuously deliver additional value as the baseline for foundation models improves.

If your product's success depends solely on the models getting smarter, you're likely building the wrong thing. Instead, create something that improves as the models advance.

Iterative feedback cycles with a diverse range of users help reveal insights that improve product execution and user experience.

Many app developers currently rely on off-the-shelf models, but infusing product knowledge into these models through prompt engineering is a new and necessary skill.