oyi77 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")
Process
- Prepare — Gather requirements, verify prerequisites, set up environment
- Execute — Run prompt engineering workflow with configured parameters
- Verify — Validate output meets requirements, document results
Verification
- All steps executed successfully
- Results validated against acceptance criteria
- Error handling tested with edge cases
- Documentation updated with findings