Generative AI tools like ChatGPT, Gemini, and Claude are large language models (LLMs) that contain vast amounts of information on various topics.
- Commercial LLMs may lack highly specialized information and can be challenging to train and deploy.
- Enterprises are developing smaller customized AI models to maximize value while safeguarding proprietary data.
- Retrieval-augmented generation (RAG) links general-purpose models to proprietary knowledge sources for effective generative AI deployment.
- Organizations can use AI-generated data to train models more efficiently and cost-effectively.
- Smaller RAG-equipped models balance privacy and problem-solving by querying local data and using synthetic data.
- Partnerships can help reduce barriers to custom AI by providing access to foundational models, workflows, and development toolkits.
Thoughts: Generative AI tools offer powerful capabilities, but the challenge lies in balancing data privacy and effective model training. The use of RAG and synthetic data can help organizations leverage AI while maintaining data security and regulatory compliance. Partnerships play a crucial role in overcoming barriers to custom AI development.