1. Prompt engineering: Extract domain-specific knowledge quickly from a generic LLM without modifying its architecture or retraining.
2. RAG (retrieval augmented generation): Merge information retrieval and LLMs to access external knowledge sources for more accurate and explainable responses.
3. Fine-tuning: Specialize a pre-trained LLM on a domain-specific dataset to improve its understanding and provide more accurate responses.
Prompt engineering is a technique that involves crafting specific questions or prompts to guide a language model in generating tailored outputs for a particular domain without the need for retraining. This method allows users to extract domain-specific knowledge quickly and efficiently, such as instructing a model to provide short answers using medical terminology.
RAG (retrieval augmented generation) combines the strengths of information retrieval and language models by connecting to external knowledge sources like databases to enhance the model’s responses. This approach is particularly useful for creating explainable and accurate answers by giving the model access to real-time or specialized information beyond its original training data, such as medical journals and research papers for a medical chatbot.
Fine-tuning involves training a pre-trained language model on a smaller, domain-specific dataset to enhance its capabilities in a particular area. For example, fine-tuning an LLM with RAG for medical knowledge can improve its understanding of medical topics, resulting in more accurate and informative responses.
These three approaches can be used independently or in combination based on project needs. While prompt engineering is the quickest way to extract domain-specific knowledge, RAG enhances the model’s responses with external information, and fine-tuning further specializes the model for a specific domain. The choice of approach depends on the project requirements, and a detailed comparison of the benefits of each technique can help inform decision-making.