Few-Shot Prompt Optimizer

Optimizes few-shot prompting strategies for large language models by selecting, ordering, and formatting demonstration examples that maximize output quality while minimizing token usage and cost.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

83.5

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

User Satisfaction (20%)

Decision Usefulness (15%)

8.5

Best for

▸Selecting optimal demonstration examples for GPT-4/Claude classification tasks using embedding similarity
▸Reducing few-shot prompt token costs by 30-50% while maintaining output quality through strategic example ordering
▸Building dynamic example retrieval systems that adapt demonstrations to each query context
▸Optimizing few-shot prompts for structured output tasks like JSON extraction or data transformation
▸Implementing bias calibration techniques to prevent few-shot examples from skewing model responses

What you'll get

●Structured few-shot prompt with 3-5 strategically ordered examples, similarity scores, and token count reduction analysis
●Python implementation of dynamic example selection using embeddings with performance benchmarks against static baselines
●Example ordering strategy with complexity graduation and recency bias optimization, including A/B testing recommendations

Not designed for ↓

×Zero-shot prompt optimization or instruction-only prompting strategies
×Fine-tuning model weights or training custom models on demonstration data
×General prompt engineering for creative writing or open-ended generation tasks
×Building chat interfaces or conversational AI systems

Expects

A specific task definition with sample inputs/outputs, target LLM, and either a candidate example pool or requirements for dynamic retrieval.

Returns

Optimized few-shot prompt with strategically selected and ordered examples, token usage analysis, and implementation guidance for static or dynamic selection.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

few-shotprompt-engineeringin-context-learningexample-selectionllmprompt-optimizationdemonstration-engineeringembedding-retrievalbias-calibrationai-promptingtoken-optimizationdynamic-selection

Research Foundation: 8 sources (6 academic, 2 official docs)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Works well with

LLM Evaluation Framework DesignerGold Prompt Engineering StrategistGold Structured Output DesignerPlatinum System Prompt ArchitectPlatinum Token Optimization StrategistGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Embedding Space ArchitectPlatinum LLM Fine-Tuning StrategistGold RAG Architecture DesignerGold

Common Workflows

Production LLM Application Optimization

End-to-end optimization of LLM applications from prompt design through production monitoring

few-shot-prompt-optimizer→Token Optimization Strategist→LLM Evaluation Framework Designer→llm-observability-engineer

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