To use Amazon Bedrock's models for tasks like classification or data extraction, you can leverage their underlying capabilities through prompt engineering and API parameter configuration. While these models are often associated with text generation, they can handle structured tasks by framing the problem as a text-in, text-out interaction. For example, to classify text sentiment, you might design a prompt like: "Classify the following text as positive, negative, or neutral: [input text]." The model will then return the classification as generated text. Bedrock models like Claude, Titan, or Cohere Command excel at following explicit instructions in prompts to perform specific operations.
Key techniques include using directive prompts to specify the task format and output constraints. For data extraction, you could instruct the model to "Extract all product names and prices from this email, formatted as JSON: [email text]." Many models support response formatting controls like JSON or XML outputs through prompt instructions. Some Bedrock models also offer specialized APIs for specific use cases—for example, Cohere's models include a "classify" endpoint that accepts examples for few-shot learning. Always check the documentation for model-specific features, as capabilities vary between providers (Anthropic Claude vs. AI21 Jurassic, for instance).
The implementation workflow involves three steps:
- Craft a test prompt with clear task instructions and example inputs/outputs
- Use the Bedrock Runtime API (via SDK or CLI) to invoke the model with your prompt
- Parse the text response into structured data using regex, JSON parsing, or other methods
For example, to extract invoice data using Amazon Titan:
import boto3
bedrock = boto3.client('bedrock-runtime')
response = bedrock.invoke_model(
modelId='amazon.titan-text-express-v1',
body=json.dumps({
"inputText": f"Extract company name, total amount, and due date from: {invoice_text}. Return JSON:"
})
)
You'd then validate and process the JSON output. Experiment with temperature settings (lower for deterministic tasks) and include validation logic to handle potential model errors.