few-shot-prompting

name	few-shot-prompting
description	Example-based prompting techniques for in-context learning
sasmp_version	1.3.0
bonded_agent	02-few-shot-specialist-agent
bond_type	PRIMARY_BOND

Few-Shot Prompting Skill

Bonded to: few-shot-specialist-agent

---

Quick Start

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

---

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

---

Shot Strategies

Strategy	Examples	Best For	Trade-offs
Zero-shot	0	Simple, well-defined tasks	Fast but less accurate
One-shot	1	Format demonstration	Minimal context usage
Few-shot	2-5	Pattern learning	Balanced accuracy/tokens
Many-shot	6-20	Complex classifications	High accuracy, high tokens

---

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:

---

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"

---

Ordering Strategies

Strategy	Implementation	When to Use
Similarity-based	Most similar to input last	Retrieval-augmented systems
Difficulty gradient	Simple → Complex	Learning/educational tasks
Random	Shuffled order	Reduce position bias
Recency	Most recent last	Time-sensitive tasks
Reverse-difficulty	Complex → Simple	Emphasize simple patterns

---

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"

---

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

---

Troubleshooting

Issue	Cause	Solution
Model copies examples	Overfitting	Add more diverse examples
Wrong format	Inconsistent examples	Standardize all formats
Missing categories	Imbalanced examples	Balance class distribution
Poor accuracy	Too few examples	Increase shot count
Token overflow	Too many examples	Reduce count, improve quality

---

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: ...

---

References

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