LLM fine-tuning
To leverage LLMs to solve your business specific problems, LLMs need to opearate on your business data. LLM models need to understand the data and knowledge of your business & industry. Here is a framework on how to adapt LLMs to solve business problems you care about. Below techniques are used to leverage the power of LLMs against your data. See the slideshow below or download
Using LLMs in Business: FineTune, RAG or Prompt (download pdf)
- Fine-Tuning: Adapts the model to specific tasks or domains, improving accuracy and relevance for specialized applications.
- RAG: Combines LLMs with external data sources to provide more accurate and up-to-date information, enhancing the model's knowledge and responses.
- Prompt Engineering: Design & create effective prompts with examples and/or instructions to the model. This guides the model's outputs, optimizing and influencing its results for specific queries or tasks.
- Model Distillation: is a process where a large language model (teacher) transfers its knowledge to a smaller model (student) for a specific task. This approach increases performance while keeping the same quality as the larger model.