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Securing and enhancing LLM prompts & outputs: A guide using Amazon Bedrock and open-source solutions
- Securing and ensuring the quality of Large Language Models (LLMs) is crucial to avoid harmful outputs and security threats such as prompt injection attacks. This post explores Amazon Bedrock's native capabilities to secure LLMs, such as built-in filtering mechanisms and sensitive information redaction. Open-source solutions like LLM Guard and DeepEval are also effective ways to ensure high-quality inputs and outputs for LLMs.
- Amazon Bedrock offers features like guardrails to protect against harmful outputs, content filters to detect and block inappropriate inputs, and denied topics and word filters to prevent the LLM from engaging in certain conversations.
- LLM Guard is an open-source solution that acts as a proxy between your application and the LLM, filtering inputs and outputs in real-time. It applies a wide range of filters to ensure inputs and outputs adhere to security protocols and offers PII detection and redaction features.
- DeepEval provides a comprehensive set of metrics to assess various aspects of your model's performance, allowing you to test and evaluate the reliability of LLMs. It enables custom evaluation metrics, automated test runs, experiments, and hyperparameters, and human feedback integration.
- To ensure high-quality LLM prompts and outputs, corporations can focus on strategies for continuously monitoring performance, refining models, and implementing thorough testing processes.
- Bringing in domain experts alongside AI teams to validate outputs can help maintain precision in critical areas.
- Organizations can leverage the native capabilities of Amazon Bedrock or open-source solutions like LLM Guard and DeepEval to secure and enhance LLM-powered applications, reducing potential risks and ensuring high-quality outputs.
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