To troubleshoot a sudden drop in model output quality after an update in AWS Bedrock, start by isolating the cause through systematic checks. First, confirm whether the model version or configuration has changed. If the model was updated, compare the current parameters (e.g., temperature, top-p, or max tokens) with those used before the update. For example, a higher temperature setting might lead to more random outputs, while a lower value could make responses overly rigid. Use Bedrock’s versioning or deployment history to verify if the update introduced unintended changes. If parameters match, test the same input with the previous model version (if available) to determine if the issue stems from the update itself.
Next, analyze input data and usage patterns. Check if recent changes to input formatting, such as altered prompts or data preprocessing, might be causing unexpected behavior. For instance, a model might perform poorly if a new prompt template removes critical context or uses ambiguous language. Validate inputs by testing a set of known, previously reliable queries to see if outputs degrade consistently. Additionally, review Bedrock’s monitoring metrics (via Amazon CloudWatch) for errors, throttling, or latency spikes that could indirectly affect output quality. If the model is part of a pipeline, ensure downstream components (e.g., post-processing scripts) haven’t been modified.
Finally, engage AWS support and leverage documentation. If the issue persists, file a support ticket with details like model ARN, timestamps, and example inputs/outputs. AWS can confirm if the update introduced known regressions or configuration changes. Simultaneously, review Bedrock’s release notes for the updated model version—some updates may require adjustments to prompts or parameters to maintain performance. For example, a model fine-tuned for specific tasks might need revised instructions if its base architecture changed. If rollback isn’t feasible, consider A/B testing the old and new versions on a subset of traffic to quantify the impact and iterate on prompt engineering or parameter tuning to adapt to the updated model.