prompt-engineering

Prompt Engineering

When to Use

Trigger phrases:

• "prompt engineering"

• "Advanced prompt engineering — chain-of-thought, few-shot, tree-of-thought, self-"

• LLM outputs are inconsistent or low quality

• Complex reasoning tasks that need step-by-step thinking

• Building reusable prompt templates for production systems

• Optimizing prompts for cost (fewer tokens) or accuracy

• Creating system prompts and custom instructions for AI agents

• Debugging prompt performance issues

• Designing multi-turn conversation flows

When NOT to Use

• When the task can be solved with existing standard libraries
• When the infrastructure is already in place and working
• When the added complexity does not provide measurable benefit

Overview

Prompt Engineering is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.

Architecture

• Input layer — Receives and validates incoming requests
• Processing layer — Core logic for system foundation
• Output layer — Formats and delivers results
• State management — Maintains context across invocations

Configuration

• Set up required environment variables and paths
• Configure logging level and output format
• Define resource limits (memory, time, API calls)
• Enable/disable features via configuration flags

Integration

• Exposes standard interfaces for other skills to consume
• Supports event-driven and request-response patterns
• Compatible with the 1ai-skills hook system
• Logs metrics for the skill performance monitor

Anti-Rationalization Table

Rationalization	Reality
"I will add monitoring later"	Without monitoring, you cannot detect failures. Add it from day one.
"One model is enough"	Different tasks need different models. Route intelligently.
"Premature optimization"	Infrastructure decisions are hard to change later. Design for scale early.

# Example: Model routing
ROUTES = {
    "code": ["claude-sonnet-4-20250514", "gpt-4o"],
    "vision": ["gemini-2.5-pro", "gpt-4o"],
    "fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}

def route_request(task: str, prompt: str):
    models = ROUTES.get(task, ROUTES["fast"])
    for model in models:
        try:
            return call_model(model, prompt)
        except Exception:
            continue
    raise RuntimeError("All models failed")