Few Shot Prompt Engineering With In Context Examples

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 prompt engineering and optimization”

23 hardest BIG-Bench tasks where models initially failed.

Unique: Provides structured few-shot exemplars that are explicitly designed for prompt engineering experimentation, enabling researchers to test prompt sensitivity and optimization strategies without task re-annotation. The dataset structure supports exemplar variation and prompt template modification.

vs others: More suitable for prompt engineering research than generic task collections because it includes curated exemplars; more flexible than fixed-prompt benchmarks because exemplars can be modified and optimized.

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 “prompt-based few-shot and zero-shot text generation”

text-generation model by undefined. 79,12,032 downloads.

Unique: OPT's few-shot capability is standard transformer behavior with no special architecture; the distinction is that it's a small, open-source model where prompt engineering limitations are more visible than in larger models, making it useful for studying prompt sensitivity

vs others: Smaller and faster than GPT-3 for prompt experimentation, but produces lower-quality few-shot results; better for research into prompt engineering mechanics than production few-shot applications

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 “prompt-engineering-and-few-shot-learning”

<br> 2.[aistudio](https://aistudio.google.com/prompts/new_chat?model=gemini-2.5-flash-image-preview) <br> 3. [lmarea.ai](https://lmarena.ai/?mode=direct&chat-modality=image)|[URL](https://aistudio.google.com/prompts/new_chat?model=gemini-2.5-flash-image-preview)|Free/Paid|

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-aggregation”

A curated list of Generative AI tools, works, models, and references

Unique: Treats prompt engineering as a first-class capability with dedicated resources and subcategories, rather than burying it within LLM documentation. Recognizes that prompt design is a critical skill for LLM application development, separate from model selection or fine-tuning

vs others: More comprehensive than single-model documentation (OpenAI's prompt engineering guide) by covering techniques across multiple models, but less interactive than specialized platforms (Prompt.com, PromptBase) which provide prompt marketplaces and community sharing

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 “prompt engineering with structured instruction design”

本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/

Unique: Provides executable prompt engineering examples showing before/after comparisons of instruction quality, demonstrating how specific design choices (role definition, context framing, output format) improve response quality; includes Chinese language prompt examples for non-English applications

vs others: More practical than theoretical prompt engineering papers because it shows runnable examples; more comprehensive than single-technique tutorials because it covers multiple instruction patterns; more accessible than research papers because it uses beginner-friendly language and Jupyter notebooks

via “prompt templating with variable interpolation and few-shot examples”

LLM framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data.

Unique: Jinja2-based prompt templating integrated into pipelines with support for variable interpolation, conditional logic, and few-shot example injection — enabling dynamic prompt construction without string concatenation

vs others: More flexible than hardcoded prompts; simpler than dedicated prompt management platforms (Prompt Flow, LangSmith) for basic use cases

via “prompt-engineering-workflow-methodology-reference”

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

Unique: Provides structured workflow methodology for prompt engineering rather than isolated technique tips, documenting the iterative design-test-refine cycle with evaluation frameworks

vs others: More systematic than scattered blog posts because it provides end-to-end workflow; more practical than academic papers because it focuses on actionable methodology rather than theoretical foundations

via “prompt optimization and few-shot example selection”

Cohere provides access to advanced Large Language Models and NLP tools.

via “prompt engineering system with agent-specific templates”

Code the entire scalable app from scratch

Unique: Implements agent-specific prompt templates that are dynamically constructed with project context, previous decisions, and feedback history. Prompts are parameterized and versioned, enabling systematic improvement of agent behavior through prompt engineering.

vs others: Unlike generic prompting approaches, GPT Pilot uses specialized, versioned prompt templates for each agent type, enabling domain-specific optimization and systematic improvement of agent behavior.

via “agent customization and fine-tuning via prompt engineering”

Marketplace for autonomous AI workers with no-code

via “prompt-optimization-and-few-shot-learning”

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

Unique: Supports sophisticated in-context learning with up to 1M token context window, enabling hundreds of examples or detailed instructions without fine-tuning — enables rapid experimentation and customization at scale

vs others: Provides faster iteration than fine-tuning-based approaches because prompts can be modified instantly without retraining, while achieving comparable accuracy to fine-tuned models on many tasks through careful prompt engineering

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 “comprehensive prompt design framework”

Guide and resources for prompt engineering.

Unique: The guide emphasizes an iterative and modular approach to prompt design, which is less common in other resources that may focus solely on static examples.

vs others: More comprehensive and structured than most prompt engineering resources, which often lack depth in practical application.