Organizations seeking to enhance the effectiveness of generative AI should train AI systems using their own data, building upon foundation models. This approach provides a more relevant context and helps address risks like inaccuracies and intellectual property concerns.
- An agritech company trains AI models using thousands of annotated images for each crop disease from its app and field teams.
- The company relies on its own sourced and vetted datasets to ensure data accuracy and reliability.
- Finetuning base models with their own data enables organizations to uphold transparency, control, and responsible AI adoption.
- Improved generative AI adoption can be achieved through standard prompt methodologies, cross-platform support, enhanced tooling, and best practices.