111 Sentiment proxy by design

ggplot(metrics, aes(x = design, y = sentiment_score, fill = design)) + geom_boxplot() + facet_wrap(~ format) + labs(title = “Sentiment proxy by design across memes”, x = “Prompt Design”, y = “Sentiment score (positive minus negative)”) + theme_minimal()

Optional: save outputs write_csv(metrics, “meme_caption_metrics.csv”) write_csv(captions, “meme_caption_texts.csv”)

Part C: Analysis and interpretation ideas - Compare prompt design A vs B: - A might yield shorter, more concise captions; B might push more creative but longer captions. - Look at lexical diversity: does B produce more varied vocabulary? - Compare sentiment: does one design skew more positive or negative, and is that desirable for your meme style? - Examine per-meme variation: - Are certain meme formats more sensitive to prompt design than others? - Are some descriptions prompting more stable outputs? - Discuss limitations: - The sentiment proxy is coarse; consider human evaluation, or model-based humor scoring if feasible. - Token limits and temperature affect output variety and safety.

Part D: Extension ideas (for groups or advanced students) - Add a second model (e.g., gpt-4o or another available model) and compare outputs. - Implement caching to avoid re-calling API for identical prompts (memoise or local CSV cache). - Create an attribution-ready report: Rmd with inline figures and a narrative analysis. - Build alt-text generation: prompt the model to produce image alt text (useful for accessibility). - Add a human-in-the-loop step: have students rate a subset of captions for humor quality, then correlate with the generated metrics.

What students submit - A reproducible R script or R Markdown file that: - Sets up the API call wrapper and prompts. - Creates the meme bank and prompt designs. - Generates captions for at least two design variants across all memes. - Produces summary metrics and at least two visualizations. - Includes a short discussion interpreting the results and any observed design effects. - A brief (1–2 page) write-up discussing: - What prompt design changes yielded noticeable differences. - Potential improvements for future iterations (e.g., different temperature, max_tokens, or a richer prompt). - Ethical considerations and policy compliance reminders when using API outputs.

Instructor tips - Start with a live demo: show a quick test caption generation to illustrate the flow from API call to tidyverse analysis. - Emphasize reproducibility: share a single Rmd that students can run with their own API keys. - Encourage version control: have students commit scripts to a repo to track changes across prompt designs. - Safety: remind students to securely manage their API keys and to monitor rate limits during live runs.

Optional starter templates (you can adapt) - API test helper: - A small function to test a single meme description and print the caption. - Two-prompt comparison: - A small pipeline that runs only two memes with both designs to illustrate the effect before scaling to all memes.

Deliverables example (short list) - R script or Rmd containing: - API wrapper and two prompt designs - Caption generation for all memes - Caption metrics (length, lexical diversity, sentiment proxy) - Two ggplot2 visualizations - A concise interpretation section

Want a fully runnable, copy-paste version? If you’d like, I can provide a compact, ready-to-run R Markdown file with all the steps wired up (including a ready-to-paste dataset, prompts, and visualizations). I’ll tailor it to your preferred OpenAI model (gpt-3.5-turbo or gpt-4), your class size, and your glossary of meme formats.

111.1 Best Practices

  • Always keep your API key secure and never hard-code it in your scripts.
  • Monitor token usage to manage costs effectively.
  • Handle errors gracefully to ensure your application remains robust.
  • Use batching and throttling to manage multiple requests and respect rate limits.
  • Regularly check OpenAI’s API documentation for updates and changes to endpoints, parameters, and best practices.

111.2 Conclusion

In this guide, we’ve covered how to generate text using OpenAI’s API in R. We’ve defined a function to interact with the API, handled responses, extracted generated text, monitored token usage, and processed multiple requests. We’ve also discussed error handling, rate limiting, and best practices for working with the API.

Now that you have the basics down, you can start experimenting with different prompts, models, and applications. The OpenAI API is powerful and flexible, allowing you to integrate AI capabilities into a wide range of projects, from chatbots to content generation to data analysis. Happy coding!