Prompt engineering

Prompt engineering is the process of designing and refining input prompts to effectively interact with large language models (LLMs) and obtain the desired outputs. It involves crafting precise, clear, and contextually relevant prompts that guide the AI to produce accurate and useful responses. This practice is essential for optimizing the performance of generative AI, ensuring that the model understands and addresses the specific needs of the user or application.

Effective prompt engineering requires a deep understanding of the model's capabilities and limitations, as well as the ability to anticipate how the model will interpret different inputs. It often involves iterative testing and adjustment, where prompts are modified based on the AI's responses to achieve better results. Techniques such as providing context, specifying the format of the output, and using examples can enhance the quality of the responses.

By mastering prompt engineering, users can leverage the full potential of LLMs for various applications, from customer support to content creation and data analysis. It enables more efficient and targeted use of AI, reducing the need for extensive post-processing of the generated outputs. Overall, prompt engineering is a critical skill for anyone looking to integrate generative AI into their workflows and achieve reliable, high-quality results.

Inside of the Infor GenAIGenAI platform you can use the Prompt Playground to try creating and testing the prompts or you can call directly the LLM Passthrough API. For the programming integration with LLM GenAI service use theLLM Passthrough API.