To debug inconsistent responses in AWS Bedrock, start by verifying input consistency and model parameters. Even minor differences in input phrasing, formatting, or context can lead to divergent outputs. For example, extra spaces, punctuation, or non-ASCII characters might cause the model to interpret similar inputs differently. Log and compare raw inputs to identify hidden variations. Additionally, check parameters like temperature (which controls randomness) and max_tokens (which limits response length). A high temperature value (e.g., 0.8) increases response variability, while a lower value (e.g., 0.2) produces more deterministic results. If responses are truncated or nonsensical, ensure max_tokens is set high enough to accommodate complete answers.
Next, examine the model’s context window and prompt engineering. Bedrock models have token limits, and if your input exceeds this, the model may truncate or misinterpret the request. For example, a 4000-token input might cause a model with a 2000-token context window to omit critical details. Use shorter, clearer prompts and test with simplified inputs to isolate the issue. Additionally, ensure your prompts include explicit instructions (e.g., "Answer concisely in 3 sentences") to reduce ambiguity. If using few-shot learning (providing examples in the prompt), validate that the examples are relevant and formatted consistently. Inconsistent examples can confuse the model, leading to erratic outputs.
Finally, test for service-side issues and model versioning. AWS Bedrock may update underlying models without explicit notice, causing behavioral changes. Confirm you’re using a specific model version (e.g., anthropic.claude-v2:1 instead of anthropic.claude-v2) if available. Check AWS CloudWatch metrics for throttling or latency issues, as degraded performance might correlate with nonsensical outputs. If the issue persists, use the Bedrock API’s logprobs or top_p features to analyze the model’s confidence in its responses. For example, low-probability tokens in a response may indicate uncertainty, signaling a need to adjust parameters or refine the input. If all else fails, contact AWS Support with specific input/output examples and parameter configurations to investigate potential backend issues.