Few Shot Learning Prompt Construction

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 “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 “few-shot-learning-demonstration”