If the output from AWS Bedrock is truncated or cuts off mid-sentence, it’s likely due to token limits or configuration settings controlling response length. Here’s how to address this:
1. Adjust the max_tokens or maxLength Parameter
Most Bedrock models allow you to set a maximum token limit for responses. If this value is too low, the model will stop generating once it’s reached, even if the response is incomplete. For example, Anthropic’s Claude models use max_tokens, while AI21 Labs’ Jurassic-2 uses maxLength. Increase this value to accommodate longer responses, but ensure it stays within the model’s maximum token capacity (e.g., Claude supports up to 100,000 tokens). Check your model’s documentation for specifics. If you’re unsure, start with a higher value (e.g., 2000 tokens) and test iteratively.
2. Review Stop Sequences and Configuration
Some models halt generation when specific stop sequences (e.g., "\n", "</answer>") are detected. If your output cuts off unexpectedly, check if your request includes unintended stop sequences. For example, a typo in a stop sequence like ". " (with a space) might cause the model to stop mid-sentence. Temporarily remove custom stop sequences to test if they’re the culprit. Additionally, avoid overly aggressive temperature or top-p values, which can sometimes lead to abrupt endings if the model struggles to generate coherent continuations.
3. Handle Input Context Limits Models have fixed context windows (e.g., 4,096 tokens for Titan, 100k for Claude). If your input prompt consumes most of this space, the model may not have enough tokens left to generate a complete response. Shorten your input or split it into smaller chunks. For example, if you’re using RAG (Retrieval-Augmented Generation), ensure retrieved documents are trimmed to essential content. For streaming responses, ensure your code aggregates all chunks—network issues or timeouts during streaming can cause incomplete results. Implement retries or error handling for streaming connections.
Example Workflow
If using the Claude model with the AWS SDK, configure max_tokens explicitly:
response = bedrock.invoke_model(
modelId="anthropic.claude-v2",
body={
"prompt": "Your prompt here...",
"max_tokens_to_sample": 4000 # Increase from default 200
}
)
Always validate the response length programmatically and retry with adjusted parameters if truncation occurs. For streaming, ensure your client waits for all chunks before processing the output.