Eliminate AI Hallucination with Amazon Bedrock Guardrails

What is AI Hallucination?

AI hallucinations is a term given to the condition when an AI model generates information based on non-existent information while appearing confident. It happens when LLMs (large language models) make incorrect assumptions based on limited or incorrect information.

For example, an AI can predict mild chest pain as indigestion while ignoring the potential for a heart attack. This misdiagnosis could result in the patient neglecting to seek timely medical care, potentially exacerbating their health condition.

Possible Reasons for AI Hallucination

AI can hallucinate responses due to multiple reasons, which are:

• Incomplete or Biased Training Data: AI models are like students—if they're given flawed or incomplete textbooks, they learn incorrect lessons. When training data lacks diversity or is biased, the model can form skewed conclusions. For example, an AI trained to detect cancer from medical images but never exposed to healthy tissue might label all tissue as cancerous.
• Lack of Grounding in Real-World Knowledge: AI often struggles to connect abstract patterns in its training data with real-world facts or physical properties, leading to hallucinations. For example, a summarization AI might invent details not present in the original text, such as fabricating non-existent quotes or facts in a news summary.
• Errors in Pattern Recognition: AI relies on recognizing patterns in data, but when encountering something unfamiliar or ambiguous, it may try to "guess" the answer, resulting in hallucinations. Research shows that over 70% of AI hallucinations stem from incorrect generalizations of learned patterns. For example, an AI chatbot asking about a fictional medical condition might fabricate symptoms, treatments, and even a medical history for the condition.
• Fabricated Context and Outputs: When the AI lacks relevant context, it may fill in the gaps with plausible-sounding but incorrect outputs. For example, a conversational AI generating web links might invent URLs or cite articles that don't exist, misleading users.
• Overfitting to Training Data: If an AI model is too closely tuned to its training data, it may struggle with scenarios outside its training scope, leading to errors. For example, a language model trained on legal documents might hallucinate legal precedents that were never established when faced with hypothetical legal questions.

What is Amazon Bedrock?

Amazon Bedrock is a comprehensive, fully managed service designed to simplify the use of generative AI. It offers access to various foundation models (FMs) from leading AI providers, including Amazon's models.

Usually, IT teams would work with these FMs separately using different APIs. However, Bedrock offers a unified API that can be used with each of these FMs. Users only need to give specific parameters to work on specific FMs.

Moreover, businesses can acquire FMs and customize them by training on their proprietary data. This enables companies to fine-tune these models to generate content or perform tasks that precisely align with their unique requirements, communication style, and operational guidelines.

Key Features of Amazon Bedrock

Here are the key features of Amazon Bedrock:

• Choice of Foundation Models (FMs): Amazon Bedrock provides access to various FMs from reputable AI companies such as AI21 Labs, Anthropic, Cohere, Stability AI, and others. This allows users to select the model that best suits their use case.
• Unified API Access: It allows you to choose different foundational models using a unified API. It makes the integration seamless as you don't have to deal with individual integrations.
• Fine-Tuning: Bedrock also allows you to customize FMs for a specific use case using fine-tuning. Fine tuning allows you to update the weights and hyper-parameters of a pre-trained AI model, helping the updated model work better for a specific task.
• Retrieval Augmented Generation (RAG): RAG enhances large language models by incorporating external authoritative knowledge bases into their outputs without retraining. This approach ensures domain-specific accuracy and relevance, leveraging LLMs' capabilities for answering questions and language translation tasks. RAG is a cost-effective way to optimize LLM performance across various contexts.
• Agent Building: You can create intelligent agents to execute tasks using enterprise systems and data sources to perform specific actions. These agents act as intermediaries between users and complex workflows, capable of interpreting input, retrieving relevant information, and executing tasks autonomously.
• Serverless and Scalable: Bedrock is a serverless service, eliminating the need for infrastructure management. It scales automatically, making it suitable for various workloads without operational overhead.
• Security and Privacy: Amazon Bedrock ensures data security, privacy, and compliance with responsible AI principles, which is critical for sensitive applications.
• Seamless Integration: The service works with existing AWS services, so integrating GenAI into your workflows will be easier if you already use them.

Amazon Bedrock Guardrails

Amazon Bedrock Guardrails is a configurable safeguard solution designed to ensure safety, privacy, and truthfulness when building generative AI applications at scale. It offers consistent and standardized safety across all supported FM. It delivers industry-leading protections by using Automated Reasoning to reduce factual errors, blocking up to 85% of harmful content and filtering over 75% of hallucinated responses in use cases like Retrieval Augmented Generation (RAG) and summarization.

As the only responsible AI capability of its kind from a primary cloud provider, Guardrails enables organizations to customize and enforce safety policies for generative AI applications. It supports various models within Amazon Bedrock, including fine-tuned and self-hosted models. With the ApplyGuardrail API, user inputs and model outputs can be independently evaluated, providing an additional layer of protection. Guardrails also helps businesses build secure and responsible AI solutions that align with ethical standards, as it can seamlessly integrate with Amazon Bedrock and Knowledge Bases.

How do Amazon Bedrock Guardrails help you combat AI Hallucinations?

Amazon Bedrock Guardrails provide advanced mechanisms to detect, mitigate, and prevent AI hallucinations, ensuring that generative AI outputs remain accurate, trustworthy, and aligned with factual information. These safeguards are adequate for use cases such as Retrieval-Augmented Generation (RAG), summarization, and conversational AI.

Amazon Bedrock Guardrails offers the following features to combat AI hallucinations:

• Contextual Grounding Checks: Guardrails support contextual grounding checks to validate whether model responses are factually accurate and relevant to the source data. For instance, in RAG use cases, responses are cross-referenced with a knowledge base to detect deviations, conflations, or invented information. This ensures that the output remains rooted in the authoritative sources.
• Automated Reasoning Checks: Amazon Bedrock Guardrail is the first safeguard that uses mathematical sound reasoning. Using this, you can automate reasoning checks to validate the logical accuracy of responses. Developers can create custom reasoning policies using organizational documents (e.g., HR guidelines or operational manuals). Guardrails then use these policies to ensure outputs align with predefined facts. For example, a legal assistant can verify generated advice against compliance documentation to prevent fabricated interpretations.
• Blocking Undesirable Topics: Organizations can define restricted topics using natural descriptions. Guardrails block user inputs or model responses related to these topics, such as a banking assistant being programmed to avoid providing investment advice and ensuring focus and compliance.
• Content Filtering for Harmful Outputs: Guardrails offer configurable filters to detect and block harmful multimodal content, such as hate speech, violence, or misconduct. For example, an e-commerce chatbot can automatically avoid generating toxic or offensive language, improving user experience while adhering to responsible AI standards.
• Redacting Sensitive Information: To protect privacy, Guardrails detect and redact sensitive data, such as personally identifiable information (PII), in model outputs. Custom redaction rules using RegEx can be defined for specific use cases, such as redacting users' credit card numbers in a financial assistant's responses.

These safeguards ensure AI applications built with Amazon Bedrock maintain factual accuracy, ethical alignment, and user trust across diverse industries and use cases.

Mactores as your Responsible AI Partner

At Mactores, we specialize in empowering businesses to harness the full potential of AWS services with efficiency, reliability, and cost-effectiveness. We bring years of expertise in cloud solutions, helping organizations integrate cutting-edge AWS technologies to drive innovation while controlling operational costs.

We are your go-to partner for building generative AI applications prioritizing accuracy, safety, and trustworthiness. We help you integrate Guardrails seamlessly into your AI systems to eliminate hallucinations, filter harmful content, and protect sensitive information while adhering to responsible AI principles.

Ready to build your responsible AI application using Amazon Bedrock guardrails?