Zero Shot And Few Shot Prompting Technique Documentation

via “zero-shot and few-shot evaluation mode switching”

11K safety evaluation questions across 7 categories.

Unique: Provides curated few-shot examples stratified by safety category (5 per category) rather than random sampling, ensuring balanced representation of each harm type. Prompt templates are explicitly customizable per model (e.g., evaluate_baichuan.py shows Baichuan-specific extraction logic), acknowledging that different architectures require different prompting strategies.

vs others: More systematic than ad-hoc few-shot selection; category-stratified examples ensure consistent coverage of all safety dimensions rather than potentially biased random sampling.

via “few-shot learning via in-context examples”

text-generation model by undefined. 92,07,977 downloads.

Unique: Leverages instruction-tuning to recognize and generalize from in-context examples without fine-tuning, enabling task adaptation through prompt engineering alone — a capability that emerges from training on diverse instruction-following datasets rather than explicit few-shot learning objectives

vs others: More practical than zero-shot for complex tasks; faster iteration than fine-tuning but less accurate than task-specific fine-tuned models

via “few-shot prompt adaptation via in-context learning”

text-generation model by undefined. 61,45,130 downloads.

Unique: Instruction-tuning enables the model to reliably recognize and follow patterns from in-context examples without explicit task specification — the model learns to infer task intent from demonstrations rather than requiring explicit instructions

vs others: More flexible than fixed-task models but less reliable than fine-tuned models; faster iteration than fine-tuning but requires more careful prompt engineering than larger models with stronger in-context learning

via “few-shot learning with in-context examples”

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Unique: Isolates few-shot learning as a distinct technique with explicit notebooks showing example selection strategies, formatting patterns, and empirical comparison of few-shot vs zero-shot performance. Uses real API calls to demonstrate token cost vs accuracy tradeoffs rather than theoretical discussion.

vs others: More systematic than ad-hoc few-shot prompting because it teaches example curation principles and provides measurable comparisons, whereas most guides treat few-shot as an afterthought to zero-shot.

via “dynamic prompt engineering and few-shot learning”

We’ve been working with automating coding agents in sandboxes as of late. It’s bewildering how poorly standardized and difficult to use each agent varies between each other.We open-sourced the Sandbox Agent SDK based on tools we built internally to solve 3 problems:1. Universal agent API: interact w

Unique: Automatically selects few-shot examples based on task similarity and integrates with agent memory to retrieve successful examples from past executions, reducing manual prompt engineering effort

vs others: More automated than manual few-shot engineering because it uses similarity-based example selection and learns from past successful executions, improving prompts over time without human intervention

via “prompt engineering technique documentation and pattern library”

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Unique: Organizes prompting techniques into a research-grounded taxonomy that connects empirical papers to practical methodologies, showing how techniques like few-shot learning relate to instruction tuning and in-context learning through shared theoretical foundations rather than treating them as isolated tricks.

vs others: Deeper than prompt engineering guides (e.g., OpenAI docs) by grounding each technique in peer-reviewed research and showing relationships between approaches; more practical than academic surveys by organizing papers by actionable technique rather than chronology.

via “zero-shot and few-shot prompting technique documentation with examples”

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Unique: Positions zero-shot and few-shot as foundational techniques that enable all other prompting methods, showing how they form the basis for more advanced techniques like CoT and ReAct

vs others: More accessible than academic papers on in-context learning because it focuses on practical application; more comprehensive than vendor tutorials because it covers both techniques and their tradeoffs

via “few-shot example injection for task specification”

Strategies and tactics for getting better results from large language models.

Unique: Provides empirically-validated guidance on example selection, ordering, and formatting specific to OpenAI models, including analysis of when few-shot outperforms zero-shot and diminishing returns thresholds

vs others: More practical and model-specific than academic few-shot learning literature, but less automated than frameworks like LangChain that programmatically select and inject examples

via “few-shot learning prompt construction”

A short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).

via “zero-shot and few-shot prompting technique documentation”

via “prompt-technique-documentation”

via “few-shot-learning-demonstration”