How can I troubleshoot if the quality of model outputs suddenly dropped, for instance possibly after a model update, when using Bedrock?

If the quality of your model outputs in AWS Bedrock suddenly drops after an update, start by confirming whether the model version or configuration has changed. AWS may update models behind the scenes, which can alter behavior even if your code remains unchanged. Check the model’s documentation or Bedrock’s release notes to identify recent updates. For example, a model like Claude-v2 might shift to a newer version (e.g., Claude-v2.1) with different default parameters or tuning. Verify your API requests explicitly specify the model version you originally tested with, and ensure parameters like temperature, top_p, or max_tokens haven’t been reset to defaults during deployment. For instance, a temperature increase from 0.2 to 0.7 could make outputs more random, leading to perceived quality loss.

Next, validate your input data and preprocessing steps. Model updates might introduce stricter input validation or changes in how prompts are interpreted. For example, a model might now truncate overly long inputs or handle special characters differently, causing unexpected outputs. Test a set of known-good inputs that previously worked well and compare results before and after the update. If outputs differ, inspect the raw API responses (using AWS CloudWatch logs) to rule out post-processing issues in your application. Also, check for encoding errors or unexpected tokenization—for instance, a model update might split compound words differently, altering context interpretation. Tools like the Bedrock API’s tokenization endpoint can help debug input handling.

Finally, use Bedrock’s monitoring tools to isolate the problem. Enable detailed logging in AWS CloudWatch to track API requests, response times, and error rates. If latency increased post-update, performance degradation might indirectly affect output quality (e.g., timeouts truncating responses). Run A/B tests by routing a subset of requests to the previous model version (if available) or a different model family (e.g., switching from Jurassic-2 to Titan). If the issue persists across models, the problem may lie in your application’s integration, such as a misconfigured retry logic or caching layer. If isolated to one model, contact AWS Support with specific examples of degraded outputs and your test results. Share minimal reproducible prompts and response comparisons to expedite troubleshooting.