A Simple Prompt Engineering Experiment Template for Text Summarization
In this second video of our Prompt Engineering series, lets walk you through a systematic framework for building and experimenting with prompts to achieve better results. We'll go through a text summarization example including task definition, setting up an evaluation metric using GPT-4 as a judge to score the quality of the summaries using a 4 point criteria, create a few prompt candidates then experiment! This video is perfect for anyone looking to deepen their understanding of AI and improve their prompt engineering skills. See you there, cheers!.
📚 Chapters
00:00 - Introduction to prompt components and experimentation process
00:32 - Starting code-based experimentation with Jupyter and Python
00:58 - Example of extracting links using the CHPT API
01:59 - Evolving the application with context information
02:50 - Optimizing output with an output indicator for desired format
03:10 - Overview of prompt components for systematic experimentation
04:00 - Setting up evaluation metrics for summarizing AI papers
05:02 - Summarizing a paper and setting up the summarization engine
05:57 - Setting up the evaluation process and criteria
06:42 - Generating prompt candidates with Python
07:17 - Turning prompt ideas into a Python list for experimentation
08:41 - Setting up an experimentation table with Pandas for prompt testing
09:35 - Concluding the experimentation process and identifying the best prompt
10:38 - Closing remarks and preview of the next module on advanced prompt engineering techniques
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