AWS sample teaches LLMs to learn from corrections, cutting costs

A new open-source sample from AWS demonstrates a method for improving LLM document extraction by learning from human corrections. This approach stores corrections as durable memory, allowing them to be reused and preventing recurring errors without the need for model retraining or redeployment. The system utilizes a tiered approach, starting with deterministic rules for common errors, then moving to cloud-based LLM extraction with confidence gating, and finally employing self-healing with few-shot examples for complex cases, ultimately reducing costs and improving accuracy over time. AI

IMPACT Accelerates enterprise adoption of LLM extraction by reducing costs and improving accuracy through a novel correction-learning mechanism.

RANK_REASON This is an open-source sample demonstrating a technique for improving LLM extraction, rather than a core model release or significant industry event.

Read on dev.to — LLM tag →

• Amazon Bedrock
• Amazon DynamoDB
• AWS
• Claude Haiku-4-5
• Claude Sonnet 4.5
• sample-prompt-correction-memory

• product
• infra

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