What if the model output I get from Bedrock is truncated or seems to cut off mid-sentence? How can I ensure I receive the full response?

If the output from Bedrock is truncated or cuts off mid-sentence, the most common cause is exceeding the model’s maximum response length limit. Bedrock models have a configurable parameter (often called max_tokens or response_length) that controls how much text the model generates. If this value is set too low, the response will end abruptly once the limit is reached. For example, if your model is configured to generate 300 tokens but the response requires 500 tokens to complete, the last 200 tokens will be missing. To fix this, increase the max_tokens value in your API request or configuration settings. However, be aware that each model has an upper token limit (e.g., 4,000 tokens for some models), so you cannot exceed that. Always check your model’s documentation for specifics.

Another approach is to manage the input length. Models have a total token limit for both input and output combined. If your input prompt is very long, it leaves fewer tokens for the response. For instance, if the total token limit is 4,096 and your input uses 3,500 tokens, the response can only be 596 tokens long. To avoid truncation, shorten the input by removing unnecessary context or splitting the task into multiple steps. For example, instead of asking the model to summarize a 10-page document in one request, break it into sections and process each separately. Additionally, use the API’s token-counting tools (like Bedrock’s built-in utilities or libraries such as tiktoken for OpenAI models) to estimate token usage before making a request.

If truncation persists, implement a programmatic check for incomplete responses. For example, detect if the response ends mid-sentence or lacks punctuation, and automatically retry the request with a higher max_tokens value. You can also use streaming to receive the output incrementally and stop when the model signals completion (e.g., via an end-of-text token). For example, with streaming enabled, you can capture the response as it’s generated and handle early termination by adjusting parameters on the fly. Lastly, structure your prompts to encourage concise answers. Explicitly ask the model to “keep responses under X tokens” or “split long answers into multiple parts.” For example, a prompt like “Explain quantum computing in 500 tokens or fewer” sets clear expectations and reduces the risk of truncation.