Fine-tuning adapts a pretrained AI model by continuing its training on a smaller, targeted dataset. The added examples can teach specialized terminology, a preferred response format, a new classification task, or behavior suited to a particular organization or domain. Updating all parameters may require substantial computing power, while parameter-efficient techniques modify only a small portion of the model or add lightweight trainable components. Fine-tuning can improve consistency and reduce the need for lengthy prompts, but it does not automatically provide current facts or eliminate errors. Dataset quality, privacy, licensing, evaluation, and safeguards remain essential. Teams should compare the result with prompting or retrieval-based alternatives, monitor performance outside the training examples, and retain a path for correcting unwanted behavior.
OpenAI introduces ChatGPT Pulse, a proactive feature delivering daily updates and suggestions directly in the app, positioning it as a potential challenger to social media feeds.
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.