Few Shot Prompting is a framework designed to improve the quality of LLM responses.
LLMs can struggle to generate outputs on previously-unseen data.
Few shot prompting attempts to solve this problem by providing a number of tangible examples alongside each prompt.
For instance, if a user wants an AI to write a professional cover letter, they might show it three different real-life samples. The LLM will then use these specific examples to learn how to write similar cover letters effectively.
What's Next
Few shot prompting is part of the LLM Optimization Techniques meta trend.
We’re seeing a growing number of techniques and approaches designed to improve LLM training, prompting and output quality.
Examples of trends in this area include synthetic data, chain of thought prompting, data augmentation, and retrieval augmented generation.
LLMs can struggle to generate outputs on previously-unseen data.
Few shot prompting attempts to solve this problem by providing a number of tangible examples alongside each prompt.
For instance, if a user wants an AI to write a professional cover letter, they might show it three different real-life samples. The LLM will then use these specific examples to learn how to write similar cover letters effectively.
What's Next
Few shot prompting is part of the LLM Optimization Techniques meta trend.
We’re seeing a growing number of techniques and approaches designed to improve LLM training, prompting and output quality.
Examples of trends in this area include synthetic data, chain of thought prompting, data augmentation, and retrieval augmented generation.