– Customers see potential in using large language models with their data for various generative AI use cases
– Ways to leverage data with foundation models include prompt engineering, retrieval augmented generation, supervised fine-tuning, reinforcement learning from human feedback, and distillation
– Choosing the right method depends on factors such as needing source citations, amount of data, specificity of desired model behavior, human involvement, personalization, budget, and serving speed required
Customers are increasingly interested in using large language models (LLMs) with their data to improve customer experiences, automate processes, access information, and create new content. There are various approaches to leveraging data with LLMs, including prompt engineering, retrieval augmented generation (RAG), supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and distillation.
Prompt engineering involves including data in the instructions given to the model, while RAG ensures model outputs are grounded in your data by searching for relevant information. SFT is useful for specific tasks like classification, while RLHF is beneficial for tasks that are not easily quantifiable or categorized. Distillation combines creating a smaller, faster model with tuning it to your specific tasks, using a larger model to teach the smaller one.
The decision on which approach to use depends on factors like the need for sourced data, the level of personalization required, the amount of data available, and the desired speed of processing. RAG is recommended for ensuring grounded outputs and control over data access, while prompt engineering may be sufficient for smaller datasets. RLHF is ideal for tasks requiring human preferences, while SFT is suitable for tasks with clear input-output pairs. Ultimately, the choice of method will depend on the specific requirements and goals of the generative AI application.