Amazon Bedrock’s models can handle tasks like classification and data extraction by adapting how you structure prompts, configure API parameters, and process outputs. Models such as Claude or Amazon Titan are designed to follow instructions, allowing you to frame these tasks as specific requests. For example, instead of asking for a story, you might provide a prompt like, “Extract all dates and product names from this text,” or “Classify this email as ‘spam’ or ‘not spam.’” The key is to give clear, task-specific guidance in your input and structure the model’s response format for easy parsing.
To implement this, you’ll use Bedrock’s API with parameters that steer the model’s behavior. For classification, you might include a system prompt like, “Analyze this support ticket and assign one of these labels: ‘billing,’ ‘technical,’ or ‘general.’” For data extraction, specify the exact fields needed (e.g., “Extract ‘invoice number,’ ‘total amount,’ and ‘due date’ from the following text”). Many models support response formatting—like JSON—which simplifies integrating outputs into applications. For instance, using Titan’s text extraction capabilities, you could process invoices by prompting the model to return structured data, then validate and store it in a database. Bedrock’s inference parameters (e.g., temperature) can also be adjusted to reduce randomness, ensuring consistent results for structured tasks.
Advanced use cases might involve combining Bedrock with other services. For example, pairing a classification model with Amazon Comprehend for sentiment analysis, or using a RAG (Retrieval Augmented Generation) approach to pull contextual data from Amazon OpenSearch. If a model supports fine-tuning (like Amazon Titan), you could train it on labeled datasets to improve accuracy for domain-specific tasks, such as medical document classification. Always test prompts iteratively—start with simple instructions, refine based on outputs, and add examples in the prompt (few-shot learning) if the model struggles. Bedrock’s flexibility lets you treat it as a versatile tool for structured tasks, not just text generation, as long as you design clear input-output workflows.