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Prompting the future
Practical AI: Machine Learning, Data Science, LLM
Building and Improving Prompt Engineering
The process of prompt engineering involves building a small data set and implementing test-driven or evaluation-driven practices to evaluate the prompt’s performance. By creating input variables based on the use case, collecting human feedback, and continuously testing the prompt, a feedback loop can be established for improvement. Connecting with real user data and feedback can enhance prompt engineering, leading to the goal of shortening the feedback loop. Additionally, monitoring and logging play a crucial role in evaluating and versioning prompts, allowing for the identification of potential loops or issues in the prompt processing.
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