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Educational Guide

What is Prompt Regression? A Complete Guide

Learn what prompt regression is, why it happens, and how to prevent it. Essential knowledge for AI engineers.

Definition

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.

What Causes Prompt Regression?

Prompt regressions can occur for several reasons: 1. **Prompt edits**: Even small changes to wording can dramatically alter outputs 2. **Model updates**: Provider model updates can change behavior 3. **Context changes**: Different system prompts or conversation history 4. **Temperature/parameter changes**: Sampling settings affect consistency 5. **Token limit issues**: Truncation can remove important context

Signs of Prompt Regression

Watch for these indicators: - Decreased accuracy on known test cases - Changes in output format or structure - Tone or style shifts - Missing information that was previously included - New errors or hallucinations - Longer or shorter responses than expected

How to Prevent Prompt Regression

Best practices for prevention: 1. **Build regression test suites**: Create datasets of expected inputs and outputs 2. **Test every change**: Run evaluations before deploying prompt changes 3. **Version control prompts**: Track all changes with version history 4. **Set quality thresholds**: Define pass/fail criteria for deployments 5. **Monitor production**: Track quality metrics in real-time

Related Topics

LLM Evaluation Metrics

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

Prompt Testing Best Practices

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

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.

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