The AI Use Cases Podcast
Join us on the AI Use Cases Podcast, where we unveil the transformative power of AI through riveting success stories. Discover how trailblazing businesses are revolutionizing their industries with cutting-edge AI innovations that you can't afford to miss.
Mastering Prompt Engineering: Few-Shot Learning, Custom Responses, and Real Data Examples
This episode begins with a welcome and an introduction to episode thirty-seven. The discussion then moves to prompt engineering with a focus on few-shot learning and the tool Prompt Poet. It explores customizing responses using YAML, Jinja2, and tone variations. Real customer data examples are used to demonstrate tone customization. The episode also covers combining these elements for coherent prompt creation. It concludes with closing thoughts on the capabilities of Prompt Poet.
Key Points
- Few-shot learning allows rapid customization of large language models without the need for extensive and costly model fine-tuning.
- Prompt Poet simplifies prompt engineering by providing a user-friendly, low-code template system that effectively manages context and integrates external data.
- By incorporating few-shot learning and dynamic templates, Prompt Poet enables the creation of AI-driven interactions that are both contextually accurate and tailored to a brand's specific tone and style.
Chapters
| 0:00 | |
| 1:09 | |
| 2:23 | |
| 5:07 | |
| 8:02 | |
| 9:08 |
Transcript
Loading transcript...
- / -