If the outputs from Amazon Bedrock are consistently poor or irrelevant, start by refining your prompts. Ensure they are specific, unambiguous, and include sufficient context. For example, instead of "Write a summary," use "Write a 150-word summary of the key features of AWS Bedrock for developers, focusing on model customization and API integration." Experiment with different phrasings or structures—sometimes adding constraints (e.g., "list three bullet points") or examples (e.g., "in the style of technical documentation") can guide the model better. Test incremental changes to isolate what improves results, such as adding step-by-step instructions or explicitly defining terms the model might misinterpret.
Next, review your model configuration and parameters. Adjust settings like temperature (lower values reduce randomness) or top_p (narrows token selection) to balance creativity and coherence. For instance, setting temperature=0.3 might yield more focused outputs for technical tasks compared to a default value. If using a specific model (e.g., Claude, Jurassic), verify it’s appropriate for your use case—some models excel at code generation, while others are better for open-ended text. Check for Bedrock’s latest model versions, as updates often improve performance. If applicable, validate that input data formats (e.g., JSON structures) align with the model’s requirements to avoid parsing errors that degrade output quality.
Finally, consider data quality and fine-tuning. If your task requires domain-specific knowledge, ensure the model has access to relevant context via retrieval-augmented generation (RAG) or by including reference material in the prompt. For recurring issues, fine-tuning the base model with a curated dataset of high-quality examples can improve alignment with your needs. For example, if generating API documentation, train on a dataset of properly formatted code snippets and their corresponding descriptions. If problems persist, consult AWS documentation for known limitations or reach out to AWS support with detailed examples of failed prompts and outputs to identify potential service-side issues or configuration oversights.