Prompting Examples
This directory contains a collection of examples demonstrating various prompting techniques for use with the Gemini API. Each notebook focuses on a specific approach to guide the model's output and achieve desired results.
Notebooks
Here's a breakdown of the notebooks available and the concepts they cover:
Add_prefixes.ipynb: Shows how to use prefixes to structure prompts for clearer model responses.Adding_context_information.ipynb: Shows how to provide context for more relevant and accurate responses.Basic_Classification.ipynb: Demonstrates how to use prompting for classification tasks, categorizing content.Basic_Code_Generation.ipynb: Demonstrates basic code generation, including error handling and generating code snippets.Basic_Evaluation.ipynb: Shows how to use the LLM for evaluation, providing feedback and grading of text.Basic_Information_Extraction.ipynb: Demonstrates extracting information from text and returning it in a defined structure.Basic_Reasoning.ipynb: Demonstrates instructing the model to solve reasoning problems.Chain_of_thought_prompting.ipynb: Guides the model through intermediate reasoning steps for complex problems.Few_shot_prompting.ipynb: Provides a few input-output examples to guide the model.Providing_base_cases.ipynb: Shows how providing base cases can influence the output.Role_prompting.ipynb: Demonstrates how to assign a specific role to the model to influence responses.Self_ask_prompting.ipynb: Demonstrates a technique where the model asks itself questions to help answer the query.Basic_Code_Review.ipynb: Demonstrates using Gemini for code review (feedback, debugging, and optimization suggestions).Zero_shot_prompting.ipynb: Demonstrates prompting the model without any specific examples.
General Tips
- Experiment: The best way to learn prompting is to experiment.
- Iterate: Prompt engineering is often an iterative process.
- Explore Other Techniques: The "Next steps" section of many of these notebooks encourages exploring other examples in the repository.