Instruction Following With Few Shot And Zero Shot Prompting

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 “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 learning prompt construction”

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

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