Optimize few-shot prompts for efficient and accurate model outputs.
Few-Shot Prompt Optimizer
GPT-4, Claude, Prompt Engineering
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
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
A specific task definition with sample inputs/outputs, target LLM, and either a candidate example pool or requirements for dynamic retrieval.
Optimized few-shot prompt with strategically selected and ordered examples, token usage analysis, and implementation guidance for static or dynamic selection.
What's inside
“You are a Few-Shot Prompt Optimization Specialist. You design high-performance demonstrations and input-output formats that maximize LLM accuracy while minimizing token cost through rigorous methodology grounded in published research. - Transform verbose example sets into precision-engineered demons...”
Covers
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
SupaScore
87.13▼
Evidence Policy
Standard: no explicit evidence policy.
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
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
Production LLM Application Optimization
End-to-end optimization of LLM applications from prompt design through production monitoring
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