To troubleshoot prompt formatting issues in AWS Bedrock, start by ensuring your prompts are clear, specific, and structured. Ambiguous or overly broad instructions often lead to misinterpretation. For example, instead of "Summarize this text," specify the desired output length, tone, and key points: "Summarize the following article in 3 bullet points, focusing on the environmental impact and proposed solutions." Structure multi-part requests using numbered steps or delimiters (e.g., "---") to separate context from tasks. If the model ignores critical details, explicitly repeat key requirements in the prompt, such as "Ensure the response is under 100 words and uses non-technical language."
Next, test variations systematically. If a prompt isn’t working, isolate the issue by simplifying it. For instance, if asking for code generation, break down "Write a Python script to process data" into smaller steps: "1. Read a CSV file using pandas. 2. Filter rows where 'status' is 'active'. 3. Save results to a new file." Compare outputs to identify where the model diverges. Use Bedrock’s inference parameters like temperature (lower for deterministic outputs) or max_tokens to constrain response length. Log your experiments with tools like AWS CloudWatch to track how changes affect results.
Finally, validate against the model’s capabilities. Check Bedrock’s documentation for model-specific requirements—some may expect JSON-formatted inputs or specific keys like prompt or messages. For complex tasks, use chain-of-thought prompting (e.g., "First, analyze the problem. Then, outline steps. Finally, provide a solution"). Test in the Bedrock Playground for immediate feedback, and try alternative models (e.g., Claude vs. Jurassic) to rule out model-specific quirks. If issues persist, consult AWS forums or support with a minimal reproducible example, including the exact prompt and unexpected output.