Learn Prompting🟢 Introduction to Few-Shot Prompting Techniques
Welcome to the Few-Shot section of the advanced Prompt Engineering Guide.
While Zero-Shot prompting is the most basic form of interaction—where the Large Language Model (LLM) Few-Shot prompting techniques it a step further. Few-Shot prompting provides the model with example pairs of problems and their correct solutions. These examples help the model better understand the context and improve its response generation.
Here are a couple of techniques we've already explored, with more on the way!
- K-Nearest Neighbor (KNN) Prompting selects relevant examples by finding the most similar cases to the input query, improving the accuracy of Few-Shot prompts.
- Self-Ask Prompting breaks down complex questions into sub-questions, and helps LLMs reason more effectively and provide better answers.
- Prompt Mining selects the optimal prompt template for a given task from a corpus of text based on the template that comes up most often in the corpus.
- Vote-K Prompting selects diverse and representative exemplars from unlabeled datasets for Few-Shot prompts.
Stay tuned for more advanced techniques, including:
- SG-ICL
Valeriia Kuka
Valeriia Kuka, Head of Content at Learn Prompting, is passionate about making AI and ML accessible. Valeriia previously grew a 60K+ follower AI-focused social media account, earning reposts from Stanford NLP, Amazon Research, Hugging Face, and AI researchers. She has also worked with AI/ML newsletters and global communities with 100K+ members and authored clear and concise explainers and historical articles.