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Top Tools and Plugins to Detect AI Hallucinations in Real-Time

by Ashley Garvin on May 16, 2025

Imagine relying on your GPS for directions, only to find yourself at a dead end. This scenario mirrors the challenge of AI hallucinations, instances where AI models generate information that appears accurate but is, in fact, incorrect. In critical sectors like healthcare, finance, and legal services, such inaccuracies can have significant consequences.

Recent studies have highlighted the prevalence of this issue. For example, research indicates that AI models used in clinical decision support systems exhibit hallucination rates ranging from 8% to 20%, depending on model complexity and training data quality . Moreover, while advancements have been made, with hallucination rates dropping from 21.8% in 2021 to 0.7% in 2025—a 96% improvement—thanks to better data, architecture, and techniques like Retrieval-Augmented Generation (RAG) , the problem persists.

To address this, several tools have been developed to detect and mitigate AI hallucinations in real-time. For instance, Pythia employs a knowledge graph to verify the factual accuracy of AI outputs, enabling real-time hallucination detection. Similarly, HDM-1 offers unmatched accuracy and real-time evaluations, setting a new standard for reliability in hallucination assessments .

Understanding and implementing these tools is crucial for organizations aiming to maintain the integrity and reliability of their AI systems. In this blog, we will delve into the top tools and plugins designed to detect AI hallucinations in real-time, exploring their functionalities, strengths, and how they can be integrated into your AI workflows to ensure accuracy and trustworthiness.

9 Tools and Plugins to Detect AI Hallucinations in 2025

1. Galileo AI

Galileo AI offers real-time detection of hallucinations in AI-generated content, providing reasoning behind flagged outputs to aid developers in understanding and correcting errors.

How It Works: Integrates with AI development environments to monitor outputs, applying rules to filter out factual inaccuracies.

Usefulness: Highly beneficial for businesses requiring immediate feedback on AI outputs, such as content creation and customer service.

Best For: Enterprises and developers focused on maintaining high-quality AI-generated content.

Pricing: Details on pricing are not specified, interested users should contact Galileo AI for more information.

Key Features: Real-time monitoring, integration capabilities, and explanatory feedback on detected hallucinations.

2. Exa Hallucination Detector

An open-source tool that verifies the accuracy of AI-generated content instantly, akin to a fact-checking version of Grammarly.

How It Works: Analyzes content and cross-references it with reliable web sources to identify inaccuracies.

Usefulness: Ideal for developers and content creators seeking a free solution to validate AI outputs.

Best For: Startups, individual developers, and small businesses.

Pricing: Free and open-source.

Key Features: Real-time fact-checking, source-backed verification, and detailed explanations for inaccuracies.

3. Genezio

Genezio provides real-time AI testing to detect false or off-topic responses, particularly in customer service applications.

How It Works: Runs live checks on AI responses, offering ongoing monitoring and actionable reports to ensure consistency and compliance.

Usefulness: Crucial for businesses that rely on AI for customer interactions, ensuring accurate and safe communications.

Best For: Customer service teams, e-commerce platforms, and industries with strict compliance requirements.

Pricing: Specific pricing details are not provided; interested users should contact Genezio directly.

Key Features: Real-time testing, industry-standard validations, and comprehensive reporting tools.

4. Patronus AI’s Lynx

Lynx is a state-of-the-art hallucination detection model that operates in real-time without the need for manual annotation.

How It Works: Utilizes advanced training techniques to identify hallucinations across various domains, enhancing the reliability of AI outputs.

Usefulness: Beneficial for organizations requiring high-accuracy AI applications across diverse fields.

Best For: Enterprises, research institutions, and sectors like healthcare and finance.

Pricing: Details on pricing are not specified, interested users should contact Patronus AI for more information.

Key Features: Real-time detection, domain versatility, and no need for manual data labeling.

5. Pythia

Pythia employs a knowledge graph to verify the factual accuracy of AI outputs, enabling real-time hallucination detection.

How It Works: Cross-references AI-generated content with a structured knowledge base to identify discrepancies.

Usefulness: Essential for businesses that require factually accurate AI outputs, such as news organizations and educational platforms.

Best For: Media companies, academic institutions, and content creators.

Pricing: Specific pricing details are not provided, interested users should contact Pythia directly.

Key Features: Knowledge graph integration, real-time verification, and adaptability to various content types.

6. Fiddler AI

Fiddler AI offers an observability platform that monitors AI models for hallucinations, safety, and compliance issues.

How It Works: Provides real-time alerts and analytics on AI outputs, focusing on metrics like toxicity and factual accuracy.

Usefulness: Vital for enterprises needing to ensure their AI systems operate within ethical and regulatory boundaries.

Best For: Large organizations, especially those in regulated industries.

Pricing: Details on pricing are not specified; interested users should contact Fiddler AI for more information.

Key Features: Comprehensive monitoring, real-time alerts, and compliance tracking.

7. Polygraf AI

Polygraf AI provides AI governance and content detection solutions, focusing on data integrity and monitoring.

How It Works: Uses forensic analyses to assess the origin and reliability of AI-generated content, ensuring adherence to privacy and regulatory guidelines.

Usefulness: Crucial for organizations that prioritize data privacy and need to monitor AI content for compliance.

Best For: Enterprises with strict data governance requirements.

Pricing: Specific pricing details are not provided; interested users should contact Polygraf AI directly.

Key Features: On-premise solutions, zero-trust interfaces, and comprehensive content monitoring.

8. Microsoft Azure AI Studio’s Correction Feature

Microsoft’s Correction feature enhances AI output accuracy by automatically detecting and correcting errors before they reach the end-user.

How It Works: Compares AI-generated content with source materials to identify and rectify inaccuracies in real-time.

Usefulness: Important for businesses that rely on AI for content generation and need to maintain high accuracy standards.

Best For: Organizations using Microsoft’s Azure AI services.

Pricing: Available as part of Azure AI Studio; pricing details depend on usage and subscription plans.

Key Features: Automatic error detection, real-time corrections, and integration with existing Microsoft tools.

9. Amazon’s Automated Reasoning Checks

Amazon Web Services (AWS) employs automated reasoning to reduce AI hallucinations, particularly in critical fields like cybersecurity and pharmaceuticals.

How It Works: Uses mathematical proofs to ensure AI behavior aligns with predefined rules and policies, enhancing output reliability.

Usefulness: Essential for industries where AI accuracy is paramount and errors can have significant consequences.

Best For: Healthcare, cybersecurity, and regulated industries.

Pricing: Details on pricing are not specified; interested users should contact AWS for more information.

Key Features: Mathematical validation, policy adherence, and integration with AWS services.

3 Common Techniques ISHIR Uses to Mitigate AI Hallucinations

1. Prompt Engineering Techniques

“According to…” Prompting: Directs the model to reference specific, reliable sources, grounding responses in factual information.

Chain-of-Verification (CoVe): Implements a multi-step process where the model generates an initial response, followed by verification questions to assess and refine the accuracy of that response.

Step-Back Prompting: Encourages the model to consider high-level abstractions before diving into detailed responses, reducing the likelihood of errors.

2. Retrieval-Augmented Generation (RAG)

Dynamic Information Retrieval: Before generating responses, the model retrieves relevant data from trusted external sources, ensuring up-to-date and accurate information is incorporated.

Source Citation: By referencing the origins of information, the model’s outputs become more transparent and verifiable.

3. Output Constraints

Length Limitation: Setting word or token limits encourages concise and focused answers.

Scope Restriction: Defining the boundaries of acceptable content ensures the model stays within relevant topics.

Final Thoughts

At ISHIR, we are at the forefront of AI innovation, with a team of certified prompt engineers dedicated to harnessing the full potential of artificial intelligence. Our experts employ advanced techniques such as Retrieval-Augmented Generation (RAG), role-based prompting, and output constraints to ensure the accuracy and reliability of AI-generated content. By integrating these methodologies, we deliver solutions that are both cutting-edge and dependable, setting new standards in AI application.

FAQs on AI Hallucination

Q1. What Are AI Hallucinations?

Ans: AI hallucinations occur when artificial intelligence systems, particularly large language models (LLMs), generate outputs that are incorrect, misleading, or entirely fabricated, despite appearing plausible

Q2. What are the most common types of AI hallucinations?

Ans: Common AI Hallucination types include:

Fabricated Facts: Generating information that sounds factual but is entirely made up.
Fake References or Citations: Inventing non-existent books, articles, authors, or research papers to back statements.
Irrelevant Outputs: Producing responses that are grammatically correct but unrelated to the input.

Q3. Can AI hallucinations be completely eliminated, or are they inherent to AI?

Ans: AI hallucinations cannot be entirely eliminated due to the inherent limitations of current AI models, which rely on pattern recognition and probabilistic predictions. While mitigation strategies like retrieval-augmented generation (RAG) and improved training data can reduce their occurrence, the possibility of hallucinations remains a fundamental challenge in AI development.