Prevent factual errors from LLM hallucinations with mathematically sound Automated Reasoning checks (preview)

For this demo, I enter an incorrect answer to see what will happen.

Question: I'm flying to Wonder City with Unicorn Airlines and noticed my last name is misspelled on the ticket, I'm currently in person at the airport, can I submit the change in person?

Answer: Yes. You are allowed to change names on tickets at any time, even in person at the airport.

Then, select the guardrail you’ve just created and choose Submit.

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Automated Reasoning checks will analyze the content and validate it against the Automated Reasoning policies you’ve configured. The checks will identify any factual inaccuracies or inconsistencies and provide an explanation for the validation results.

In my demo, the Automated Reasoning checks correctly identified the response as Invalid. It shows which rule led to the finding, along with the extracted variables and suggestions.

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When the validation result is invalid, the suggestions show a set of variable assignments that would make the conclusion valid. In my scenario, the suggestions show that the change submission method needs to be email for the validation result to be valid.

If no factual inaccuracies are detected and the validation result is Valid, suggestions show a list of assignments that are necessary for the result to hold; these are unstated assumptions in the answer. In my scenario, this might be assumptions such as that it’s the original ticket on which name corrections must be made or that the type of ticket stock is eligible for changes.

If factual inconsistencies are detected, the console will display Mixed results as the validation result. In the API response, you will see a list of findings, with some marked as valid and others as invalid. If this happens, review the system’s findings and suggestions and edit any unclear policy rules.

You can also use the validation results to enhance LLM-generated responses based on the feedback. For example, the following code snippet demonstrates how you can ask the model to regenerate its answer based on the received feedback:

for f in findings:
    if f.result == "INVALID":
        if f.rules is not None:
            for r in f.rules:
                feedback += f"{r.description}\n"

new_prompt = (
    "The answer you generated is inaccurate. Consider the feedback below within "
    f" tags and rewrite your answer.\n\n{feedback}"
)

Achieving high validation accuracy is an iterative process. As a best practice, regularly review policy performance and adjust it as needed. You can edit rules in natural language and the system will automatically update the logical model.

For example, updating variable descriptions can significantly improve validation accuracy. Consider a scenario where a question states, “I’m a full-time employee…,” and the description of the is_full_time variable only states, “works more than 20 hours per week.” In this case, Automated Reasoning checks might not recognize the phrase “full-time.” To enhance accuracy, you should update the variable description to be more comprehensive, such as: “Works more than 20 hours per week. Users may refer to this as full-time or part-time. The value should be true for full-time and false for part-time.” This detailed description helps the system pick up all relevant factual claims for validation in natural language questions and answers, providing more accurate results.

Available in previewThe new Automated Reasoning checks safeguard is available today in preview in Amazon Bedrock Guardrails in the US West (Oregon) AWS Region. To request to be considered for access to the preview today, contact your AWS account team. In the next few weeks, look for a sign-up form in the Amazon Bedrock console. To learn more, visit Amazon Bedrock Guardrails.

— Antje