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
content refresh 2026-07: freshness review findings fixed (stale claims, invented precision, missing 2026 practice)
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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