Create structured few-shot prompts with examples for consistent LLM outputs.
npx skills add https://github.com/xiaoshuangLi/aibo --skill few-shot-prompting
Please help me install this Agent Skill. Skill: few-shot-prompting Source: https://github.com/xiaoshuangLi/aibo/tree/main/skills/few-shot-prompting Command: npx skills add https://github.com/xiaoshuangLi/aibo --skill few-shot-prompting
Few-shot prompting dramatically improves LLM output quality and consistency by providing 2-8 examples of the desired input→output behavior, enabling rapid task teaching without fine-tuning.
Create a prompt with 3-5 representative examples and a clearly defined output format, then test on real inputs.
High-intent search queries and answers about installing and using this skill.
Use few-shot prompting to improve LLM output consistency by providing 2-8 structured input-output examples, enabling rapid task teaching without fine-tuning.
Create JSON output few-shot prompts by providing 3-5 representative examples with clearly defined structured JSON formats to enforce consistent outputs from LLMs.
Chain-of-thought few-shot prompting guides complex reasoning and code transformation tasks by including step-by-step reasoning traces within the provided examples.
Include 3-5 representative examples in a few-shot prompt for data extraction, ensuring diverse selections that cover edge cases for reliable transformations.
Avoid few-shot prompting when tasks are highly ambiguous or token limits are critical, as providing 2-8 examples increases prompt length and consumes context window capacity.
alexsandrocruzDesign production prompt templates with few-shot and chain-of-thought patterns.
trymiraiValidates inference outputs by comparing them with reference results via Rust cargo test harness.
P47PhoenixDesign complete, explainable, and reusable prompts for LLMs and AI systems.
camoneartDesign and optimize production prompts and templates for large language models.
