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Natural Language Processing Systems • Prompt Engineering & ManagementHard⏱️ ~3 min

Prompt Management: Versioning, Evaluation, and A/B Testing

Prompt Versioning
Prompts need version control separate from code. Why separate? Prompts change more frequently (often daily during optimization), non-engineers may need to edit them (product managers, content writers), and prompt changes require different testing than code changes. A dedicated system tracks each version with metadata: author, timestamp, rationale for change, and measured performance on test cases.
A typical version entry: Version 47 by Sarah on March 15, changed from "Summarize the following" to "Write a 2-3 sentence summary of the key points." Accuracy improved from 78% to 84% on test set. This history is invaluable for debugging production issues - you can pinpoint exactly when a problem started.
Evaluation Frameworks
Systematic evaluation requires test suites covering normal cases, edge cases, and adversarial inputs. Define metrics appropriate to your task: accuracy for classification, ROUGE or BLEU for summarization, human preference scores for open-ended generation. Run evaluations automatically on prompt changes before deployment. Track metrics over time to detect drift.
Evaluation datasets must be representative but not overfit. If you tune prompts obsessively on your test set, you optimize for those specific examples rather than general performance. Hold out a separate validation set that you check only occasionally.
💡 Key Insight: Prompt A/B tests need larger sample sizes than UI tests. LLM outputs have higher variance than button clicks. A UI test might conclude at 500 users. A prompt test might need 5000 requests for statistical significance.
A/B Testing
Before deploying prompt changes to all users, route 10% of traffic to the new version while 90% uses the current version. Compare metrics after 1000-5000 requests. Set clear success criteria before the test: "New version must achieve equal or better accuracy with no more than 10% latency increase." Without predefined criteria, there is pressure to ship regardless of results.

💡 Key Takeaways

✓Prompt versioning needs separate system from code: prompts change daily, non-engineers edit them, testing requirements differ

✓Each version tracks author, timestamp, rationale, and measured performance - essential for debugging production issues

✓Evaluation requires test suites with normal cases, edge cases, adversarial inputs - hold out validation set to avoid overfitting

✓A/B tests need 5000+ requests (not 500) for significance due to high LLM output variance - set success criteria before testing

📌 Interview Tips

1Describe version entries: who changed what, why, and measured impact (78% to 84% accuracy). Shows how to debug.

2Warn about test set overfitting: if you tune obsessively on test data, you optimize for those examples, not general performance.

3Give specific A/B test guidance: 10% traffic split, 1000-5000 requests needed, predefined success criteria.

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