Part 3/7:
Once a value-driven use case is identified, organizations should begin by leveraging prompt engineering. This technique involves crafting effective prompts to steer the model's responses, a relatively straightforward approach that requires minimal technical expertise and resources.
If prompt engineering alone is insufficient for customizing the model to specific needs, the next step is Retrieval-Augmented Generation (RAG). RAG combines retrieval systems with generation models to improve accuracy and relevancy, further tailoring AI outputs.
For more specialized or proprietary requirements, fine-tuning the models becomes advantageous. Fine-tuning involves training the LLM on a company's specific dataset, enabling the model to better understand and respond within a particular domain.