3 Ways to Use Chain-of-Thought and Few-Shot Prompting
Important note: This training may demonstrate AI tools that are not approved for use with Stanford data. Inclusion in this session does not imply institutional approval. Participants should refrain from entering Stanford data into unapproved tools. An up-to-date list of approved and reviewed tools is available on the GenAI Evaluation Matrix page.
| Code | Date | Delivery | Cost |
|---|---|---|---|
| ITSB-1012 |
|
Live Online : 1 session | $150 |
Before each live online session, Tech Training will provide a Zoom link for live online classes, along with any required class materials.
Explore three practical approaches to Chain-of-Thought and Few-Shot prompting. Practice techniques that help AI reason through complex problems and produce outputs that match your specific context and standards.
- Program Description
This session introduces participants to two powerful prompting techniques: Chain-of-Thought, which guides AI to show its reasoning step by step, and Few-Shot prompting, which trains AI on your own examples before generating new content. Participants explore three applied approaches and practice using both techniques across research, analysis, and writing scenarios.
- Learning Objectives
Learners will have the opportunity to:
1. Explore how Chain-of-Thought and Few-Shot prompting differ from standard prompts
2. Practice applying CoT prompting to reason through data anomalies and complex scenarios
3. Work with Few-Shot examples to generate outputs that reflect your own standards and style
4. Experiment with combining both techniques for more precise and grounded results
5. Identify which approach best fits different types of tasks in your work- Topic Outline
Topics:
- Overview of Chain-of-Thought and Few-Shot prompting
- Three applied prompting approaches
- Building effective Few-Shot example sets
- Using CoT to analyze and reason through complex data
- Choosing the right technique for the taskTools used: Stanford AI Playground and free versions of other AI models (ChatGPT, Gemini, or Claude)
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