Few-Shot Prompting Skill

Few-Shot Prompting Skill

Bonded to: few-shot-specialist-agent

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Quick Start

Skill("custom-plugin-prompt-engineering:few-shot-prompting")

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Parameter Schema

parameters:
  shot_count:
    type: integer
    range: [0, 20]
    default: 3
    description: Number of examples to include

  example_format:
    type: enum
    values: [input_output, labeled, conversational, structured]
    default: input_output

  ordering_strategy:
    type: enum
    values: [random, similarity, difficulty, recency]
    default: similarity

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Shot Strategies

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Core Patterns

1. Standard Input-Output

[Task instruction]

Example 1:
Input: [example_input_1]
Output: [example_output_1]

Example 2:
Input: [example_input_2]
Output: [example_output_2]

Example 3:
Input: [example_input_3]
Output: [example_output_3]

Now process:
Input: [actual_input]
Output:

2. Labeled Classification

Classify the following text into categories: [category_list]

"[text_1]" → [category_1]
"[text_2]" → [category_2]
"[text_3]" → [category_3]

"[new_text]" →

3. Structured Output

Extract information in the specified format.

Text: "John Smith, CEO of TechCorp, announced the merger on Monday."
Output: {"name": "John Smith", "title": "CEO", "company": "TechCorp", "action": "announced merger", "date": "Monday"}

Text: "Dr. Sarah Chen presented findings at the 2024 AI Conference."
Output: {"name": "Sarah Chen", "title": "Dr.", "event": "2024 AI Conference", "action": "presented findings"}

Text: "[new_text]"
Output:

4. Chain-of-Thought Few-Shot

Solve the following problems showing your reasoning.

Problem: If a shirt costs $25 and is on 20% sale, what's the final price?
Reasoning: 20% of $25 = $25 × 0.20 = $5 discount. Final price = $25 - $5 = $20.
Answer: $20

Problem: [new_problem]
Reasoning:
Answer:

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Example Selection Criteria

selection_criteria:
  diversity:
    coverage: "Include all output classes/​categories"
    variation: "Vary input complexity and length"
    edge_cases: "Include at least one boundary case"

  quality:
    correctness: "All examples must have correct outputs"
    clarity: "Examples should be unambiguous"
    representativeness: "Reflect real-world distribution"

  relevance:
    similarity: "Examples similar to expected inputs"
    domain: "Match the target domain/​context"
    recency: "Use recent examples for time-sensitive tasks"

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Ordering Strategies

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Token Optimization

optimization_techniques:
  concise_examples:
    description: "Use minimal but complete examples"
    savings: "~25%"
    example:
      verbose: "The customer said 'This product is amazing!' which expresses positive sentiment"
      concise: "'Amazing product!' → positive"

  shared_prefix:
    description: "Factor out common instructions"
    savings: "~15%"
    implementation: "Move repeated text to instruction section"

  dynamic_loading:
    description: "Only load relevant examples"
    savings: "~40%"
    implementation: "Use semantic search to select examples"

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Validation

validation_checklist:
  format:
    - [ ] All examples use identical structure
    - [ ] Separators are consistent
    - [ ] Input/​output markers are clear

  content:
    - [ ] Examples cover all output categories
    - [ ] No duplicate examples
    - [ ] Edge cases included

  quality:
    - [ ] All outputs are correct
    - [ ] No example leakage (test data in examples)
    - [ ] Complexity is varied

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Troubleshooting

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Integration

integrates_with:
  - prompt-design: Base prompt structure
  - chain-of-thought: Reasoning examples
  - prompt-evaluation: Test effectiveness

combination_example: |
  # Few-shot + CoT
  [Instruction]

  Example 1:
  Input: [problem]
  Reasoning: [step-by-step]
  Output: [answer]

  Example 2: ...

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References

See references/​GUIDE.md for example selection strategies. See assets/​config.yaml for configuration options.