Prompt Engineering Playbook for Enterprise Applications
A practical guide to designing, testing, and optimizing prompts for production AI systems — from basic techniques to advanced chain-of-thought and few-shot patterns.
## Prompt Engineering Playbook
Effective prompts are the difference between demo-quality and production-quality AI. This playbook covers the techniques we use across the uFlo.ai portfolio.
### Foundation Techniques - Role assignment: "You are a [domain expert] who [specific task]" - Output formatting: Specify exact structure (JSON, markdown, tables) - Constraint setting: Define boundaries, edge cases, and failure modes - Example provision: 2-3 examples dramatically improve consistency
### Advanced Patterns - Chain-of-thought: "Think step by step" for complex reasoning - Few-shot learning: Provide input-output examples for pattern matching - Self-consistency: Generate multiple responses and select the most common - Tree-of-thought: Explore multiple reasoning paths for complex decisions
### Production Optimization - Temperature tuning: Lower (0.1-0.3) for consistency, higher (0.7-0.9) for creativity - Token budgeting: Balance quality against cost and latency - Prompt versioning: Track prompt changes like code changes - A/B testing: Measure prompt variants against real-world outcomes
### Enterprise Considerations - PII handling: Strip sensitive data before prompt injection - Hallucination detection: Implement verification layers for critical outputs - Audit trails: Log all prompts and responses for compliance - Fallback chains: Define escalation paths when confidence is low
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