Prompt Engineering and Retrieval-Augmented Generation as Solutions

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The use of prompt engineering has been surprisingly effective in minimizing the need for fine-tuning in AI models, with a focus on integrating factual context. As models are trained on public internet data, they lack insight into private information, creating the need for a hybrid system of search or information retrieval combined with generation. Retrieval augmented generation (RAG) has emerged as a powerful solution to this challenge, providing an effective approach to integrating private and up-to-date information into AI models.

Small changes in prompts can create large changes in the output behavior of generative AI models. Add to that the confusion around proper evaluation of LLM applications, and you have a recipe for confusion and frustration. Raza and the Humanloop team have been diving into these problems, and, in this episode, Raza helps us understand how non-technical prompt engineers can productively collaborate with technical software engineers while building AI-driven apps.

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