A Simple Prompt Engineering Experiment Template for Text Summarization
AI Summary
Summary: Prompt Engineering and Experimentation Process
- Prompt Components & Engineering Template
- Discussed components of a prompt.
- Introduced a framework for systematic prompt experimentation.
- Experimentation with Code
- Demonstrated prompt experimentation using a Jupyter notebook and Python.
- Example: Extracting links from text using the OpenAI API.
- Code Example: Link Extraction
- Initialized OpenAI client.
- Sent prompt to extract links, specifying desired behavior.
- Broke down prompt into parts for clarity and evolution.
- Added context and output indicators to refine results.
- Used Python’s
literal_evalto convert output to a usable list.- Prototyping Prompts
- Defined a task: Summarize an AI paper for a non-technical audience.
- Set evaluation metrics using GPT-4 as a judge:
- Relevance, Coherence, Consistency, Fluency.
- Created a summarization engine context in the system message.
- Evaluation Function
- Developed a function to score summaries based on set criteria.
- Tested the function to ensure it returns an integer score.
- Generating Prompt Candidates
- Discussed generating multiple prompts for experimentation.
- Created a function to generate prompts using the OpenAI API.
- Converted generated prompt suggestions from a string to a Python list.
- Experimentation Table Setup
- Used Pandas to create a table for experimentation data.
- Included columns for prompt, score, model, and output.
- Running the Experiment
- Looped over prompt candidates to generate and evaluate summaries.
- Stored results in the table and identified the best prompt based on scores.
- Conclusion
- Completed the loop of the experimentation process.
- Laid the foundation for systematic prompt experimentation and evolution.
- Prepared for the next module on advanced prompt engineering techniques.
Next Steps
- Explore more advanced prompt engineering frameworks and techniques in the next module.