Accelerating custom entity recognition with Claude tool use in Amazon Bedrock

2026-03-24 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Data Science & Analytics · Depth: Intermediate, long

Summary

AWS introduces Claude Tool use in Amazon Bedrock, enabling dynamic, adaptable entity recognition from unstructured data without extensive model training. This serverless solution leverages large language models (LLMs) to extract structured information, such as driver's license fields (names, dates, addresses), from various document types using natural language prompts. The architecture integrates Amazon S3 for document storage, AWS Lambda for event-driven processing, Amazon Bedrock with Anthropic Claude 4.5 Sonnet for entity extraction, and Amazon CloudWatch for monitoring. The implementation guide details setting up the environment, creating a Python 3.12 Lambda function with a 30-second timeout, defining JSON schemas for tool use, configuring S3 event notifications, and testing the solution with image inputs up to 20 MB and 4096x4096 pixels.

Key takeaway

For MLOps Engineers building document processing pipelines, Claude Tool use in Amazon Bedrock offers a scalable, cost-effective method for custom entity extraction. You should consider this serverless approach to automate information retrieval from unstructured data, minimizing traditional ML model training and maintenance. Implement robust error handling and monitor CloudWatch Logs to ensure reliable operation and fine-tune performance.

Key insights

Claude Tool use in Amazon Bedrock enables dynamic, serverless entity extraction from unstructured data using LLMs and function calling.

Principles

• Augment LLM capabilities via external function invocation.
• Define tools with JSON schemas for structured input/output.
• Automate document processing with serverless architecture.

Method

Define tools with names and input schemas. Provide a user prompt. Claude evaluates the prompt, selects tools, and invokes them with appropriate inputs to extract structured data.

In practice

• Use "tool_choice": "auto" for dynamic tool selection.
• Set Lambda timeout to 1-2 minutes for image processing.
• Crop and de-skew images for better OCR accuracy.

Topics

• Claude Tool use
• Amazon Bedrock
• Serverless Architecture
• Entity Extraction
• Large Language Models

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.