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Prompt Testing Best Practices for Production AI

Essential best practices for testing LLM prompts. Build reliable AI applications with systematic testing.

Definition

A collection of proven methods and workflows for systematically testing LLM prompts to ensure quality, reliability, and safety in production applications.

Build Comprehensive Test Datasets

Your test data should include: - **Happy path cases**: Common, expected inputs - **Edge cases**: Unusual or boundary inputs - **Adversarial cases**: Attempts to break the system - **Regression cases**: Previously failed scenarios - **Real user queries**: Anonymized production data

Test Every Change

Integrate testing into your workflow: - Run evals before merging prompt changes - Automate testing in CI/CD pipelines - Compare new versions against baselines - Block releases that fail quality gates

Version Control Your Prompts

Treat prompts like code: - Store prompts in version control - Track all changes with meaningful commits - Enable rollback to previous versions - Document the reasoning behind changes

Monitor Production Quality

Don't stop at pre-deployment testing: - Sample and evaluate production outputs - Track quality metrics over time - Set up alerts for quality degradation - Feed issues back into test datasets

Related Topics

Prompt Regression

A prompt regression occurs when changes to a prompt or its context cause the LLM to produce lower-quality outputs than before. This can happen due to prompt edits, model updates, or changes to system context.

Quality Scoring

Quality scoring is the process of systematically evaluating LLM outputs against defined criteria to produce numerical scores that enable comparison and threshold-based decisions.

LLM Evaluation Metrics

LLM evaluation metrics are quantitative and qualitative measures used to assess the quality, accuracy, and usefulness of large language model outputs.

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