Evaluation-Driven Prompt Design

Expert

Design prompts by first defining evaluation metrics and test cases.

01 The Concept

Instead of writing prompts and hoping they work, evaluation-driven design starts with defining success metrics (accuracy, format compliance, latency) and building test cases first. Prompts are then iterated until they consistently pass all evaluation criteria.

02 Weak vs. Strong

EX 01Evaluation-First Workflow

# Step 1: Define metrics metrics = { 'accuracy': >= 0.95, 'format_valid_json': True, 'latency_ms': <= 2000, 'no_hallucination': True } # Step 2: Build test cases tests = load_golden_dataset('classification_tests.json') # 100 cases # Step 3: Iterate prompt until passing while not all_metrics_pass(prompt, tests, metrics): prompt = refine_prompt(prompt, failure_analysis)

→ Why it works

Metric-first design eliminates guesswork and provides objective evidence that prompts are production-ready.

03 Key Points

01Metric definition: Establishing measurable success criteria before writing prompts.

02Test case design: Creating representative input-output pairs for validation.

03Iterative refinement: Modifying prompts based on test failure analysis.

04A/B testing: Comparing prompt variants on identical test sets.

05Continuous monitoring: Tracking production metrics to detect prompt drift over time.

04 Model-Specific Notes

Claude handles Evaluation-Driven Prompt Design tasks with excellent instruction compliance and structured output formatting.

05 For Your Role

Think of Evaluation-Driven Prompt Design as organizing your work systematically so every step is clear and repeatable.

Next: Cascading Fallback Chains →