prompt-engineering

Prompt Engineering Skill

Overview

The prompt-engineering skill establishes a disciplined approach to LLM instruction design within the KILO-KIT ecosystem. Moving beyond ad-hoc prompting, this skill treats prompts as first-class code, emphasizing contract-based structures, declarative signatures, and rigorous validation loops to ensure reproducible, high-quality AI behavior.

When To Use

• When developing new LLM-powered features or agents.
• When existing prompts produce inconsistent, fragile, or hallucinated outputs.
• When implementing complex reasoning tasks that require strict output formatting.
• When you need to scale prompt maintenance across a team or large codebase.
• When setting up automated prompt optimization or regression testing pipelines.

Core Concepts

Contract-First Prompt Architecture

Prompts are defined using a 5-part structure to ensure clarity and modularity:

1. Identity: Define the persona, expertise, and operational boundaries.
2. Context Boundaries: Explicitly define what data is in-scope and what is off-limits.
3. Operational Rules: Step-by-step logic and prioritized directives.
4. Edge Cases: Explicit handling of ambiguous, empty, or adversarial inputs.
5. Output Schemas: Declarative JSON, XML, or Pydantic schemas to enforce structured output.

Reasoning Model Steerability

Optimizing for advanced reasoning models (e.g., o1, o3, Gemini 2.0+):

• Reasoning Effort Control: Explicitly specify constraints to trade-off speed vs. reasoning depth.
• Chain-of-Symbol (CoS): Use compact symbol-based notation for complex logic to minimize token usage and improve coherence.
• XML/Markdown Boundary Formatting: Utilize strict XML tags (e.g., , , ) to segment reasoning from content.

DSPy Integration

Leverage programmatic prompt optimization:

• Signatures: Define declarative Input/Output contracts.
• Optimizers: Apply BootstrapFewShot, MIPROv2, or COPRO to automatically refine prompts based on validation datasets.