To troubleshoot issues with how you format prompts for Bedrock, start by analyzing clarity and structure. Bedrock, like many AI systems, relies on explicit instructions and context to generate accurate responses. If your prompt is misinterpreted, the first step is to check for ambiguity. For example, if you ask, “Summarize the data,” Bedrock might not know which data you’re referring to or the desired format. Instead, specify details: “Summarize the sales data from Q1 2023 in a bulleted list, focusing on regional revenue growth.” Use clear delimiters (like triple quotes or section headers) to separate instructions from input data. For instance, structure your prompt as:
Instruction: "Convert this JSON data into a CSV format."
Input data: "{...}"
This reduces confusion about what’s being asked. Additionally, avoid vague verbs like “process” or “analyze” without explaining how—instead, say “extract the top 5 results” or “calculate the average.”
Next, test your prompts iteratively. Start with a minimal version of your request and gradually add complexity. For example, if a prompt like “Generate Python code to sort a list” returns incorrect results, break it down: First, ask, “Write a function to sort a list in ascending order,” then add constraints like “Handle duplicate values” or “Use merge sort algorithm.” Version your prompts (e.g., v1: basic sort, v2: sort with duplicates) to track changes and outcomes. Use A/B testing by sending slightly altered prompts to see which phrasing Bedrock handles better. If the model consistently misinterprets a specific term, replace it—for instance, use “convert” instead of “transform” if the latter leads to unexpected outputs. Validate responses against predefined criteria (e.g., output format, required keys in JSON) to identify where the misunderstanding occurred.
Finally, address common pitfalls. Overloading a prompt with multiple tasks (e.g., “Parse this JSON, validate the fields, and log errors”) can cause Bedrock to prioritize one part and ignore others. Split complex requests into sequential steps or separate prompts. Another issue is assuming Bedrock knows your domain-specific terminology without explanation. For example, if you ask, “Normalize the dataset using Z-score,” clarify whether you mean per-feature normalization or a global calculation. Also, ensure you’re using Bedrock’s supported formats—if it expects a specific template for code generation, follow that structure. For debugging, use Bedrock’s logging or validation features (if available) to trace how the prompt was processed. If the issue persists, consult the documentation for guidance on syntax or constraints, such as character limits or reserved keywords that might interfere with your instructions.