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How CustomGPT Mitigates AI Hallucinations

13 Feb 2025 / 14 minutes to read

Elena Daehnhardt


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Introduction

Large Language Models (LLMs) sometimes create information that looks real but is incorrect or made up. This is especially problematic in critical areas like medicine, law, or finance, where even minor errors can cause harm.

Reducing AI hallucinations

The recent survey paper by Tonmoy et al. A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models explain the main techniques to reduce AI hallucinations with prompt engineering and model development:

  1. Prompt engineering with:
    1. Retrieval Augmented Generation:
      • Before Generation: Retrieve accurate external information to guide responses.
      • During Generation: Check and correct information step-by-step as it’s generated.
      • After Generation: Revise outputs to align them with verified data.
      • End-to-End Approaches: Combine retrieval and generation seamlessly for accuracy.
    2. Self-Feedback and Refinement: Some methods improve model outputs by providing feedback to the model about its mistakes. This iterative process helps refine answers to make them more accurate over time.
  2. Model development:
    1. New Decoding Strategies: These methods focus on how the model generates text step by step:
      • Context-Aware Decoding (CAD): Ensures the model pays attention to the context when generating responses, overriding its internal biases.
      • DoLa (Decoding by Contrasting Layers): Looks at patterns in the model’s layers to spot and avoid hallucinations during text generation.
      • Inference-Time Intervention (ITI): Adjusts the model’s thinking process while answering to make its outputs more truthful.
    2. Using Knowledge Graphs (KGs): These are like structured databases of facts and relationships. Some models use KGs to make their answers more grounded and accurate:
      • RHO: Combines information from a KG with the dialogue to ensure the response matches real-world knowledge.
      • FLEEK: Highlights errors in text by comparing it with facts from KGs or the web and suggests corrections
    3. Faithfulness-Based Loss Functions: These are new ways of training models to prioritise accuracy:
      • THAM Framework: Penalises the model when it copies text without understanding the context, especially in video-based conversations.
      • Loss Weighting: Adjusts the importance of training data based on how well it matches the facts.
    4. Supervised Fine-Tuning: Involves retraining models using carefully prepared datasets to teach them better behaviour:
      • Knowledge Injection: Adds domain-specific knowledge during training to reduce hallucinations.
      • Teacher-Student Models: Uses a smarter model (teacher) to guide a smaller model (student) in learning accurate answers.
      • HAR (Hallucination Augmented Recitations): Creates challenging datasets to train models to better ground their answers in facts.

These methods involve adjusting how the model thinks during generation, providing it with better factual resources, teaching it to be more accurate during training, or combining these approaches for more robust performance.

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models by Towhidul Islam Tonmoy with coauthors serves as a guide for making LLMs more trustworthy and practical for real-world use. The survey categorised over 30 approaches based on their design, such as retrieval-based methods, prompt adjustments, or new training techniques. This classification makes it easier to understand and apply these methods [1].

Existing techniques have limitations, like dependency on external tools, increased computational demands, or incomplete solutions for real-world tasks [1]. Authors suggest combining methods, improving evaluation metrics, and focusing on ethical concerns to build safer and more reliable AI systems [1].

What is CustomGPT?

There are pioneers in AI hallucination mitigation such as CustomGPT.AI.

CustomGPT.AI is a specialized variant of GPT (Generative Pre-trained Transformer) that can be fine-tuned and customized for specific applications and domains, as explained in How to Build Your Own Custom GPT: A Comprehensive Guide to OpenAI and CustomGPT.ai. CustomGPT offers several advantages in addressing AI hallucinations by leveraging domain-specific knowledge and tailored training data.

You can try out CustomGPT.AI for free now. Please let me know what do you think, is it better than chatGPT and Friends?

Tackling AI Hallucinations

Here’s how CustomGPT.AI can help in tackling AI hallucinations (read more in How To Stop ChatGPT From Making Things Up – The Hallucinations Problem):

  1. Understanding the Problem: AI hallucinations occur when the chatbot generates incorrect or made-up information, leading to potential business issues like misinformation, reduced customer trust, and compliance risks.

  2. The Context Boundary Feature:
    • CustomGPT.AI introduces a “context boundary wall” to ensure its responses strictly come from the specific business data provided to it.
    • This boundary prevents the AI from making up information or pulling in irrelevant data from the internet or general sources.
  3. How It Works:
    • Prompt Engineering: CustomGPT.AI uses advanced techniques to guide the AI’s focus on relevant data, steering responses toward accurate information.
    • Proprietary Pre-Processing: It carefully manages the context sent to the AI during each query, ensuring the chatbot remains within the business’s content limits.
  4. Benefits for Businesses:
    • Ensures responses align with brand values, data, and operational context.
    • Reduces the risk of misinformation, boosting customer confidence and engagement.
    • Helps avoid false product recommendations, inaccurate customer support answers, and compliance violations.
  5. Testing and Reliability:
    • Businesses can test the boundary by asking off-topic questions. The AI should either decline to answer or give a neutral response.
    • Regular testing ensures the system maintains consistent and accurate behaviour over time.

By using these methods, CustomGPT.AI minimises hallucinations, providing businesses with reliable and on-brand AI interactions.

In short, CustomGPT.AI ensures that all responses strictly come from your business content, eradicating the risk of generating unrelated or inaccurate information. This feature prevents the chatbot from recommending competitors, outputting falsehoods, or using irrelevant information, increasing trust and brand integrity.

Businesses can leverage AI’s power while retaining control over the output, ensuring alignment with company data, brand voice, and operational realities.

Benefits of CustomGPT

  1. Domain-Specific Training:
    • CustomGPT.AI models can be trained on domain-specific data, ensuring that the model has a deeper understanding of the subject matter. This reduces the likelihood of generating inaccurate or nonsensical information.
    • For example, a CustomGPT.AI model for the medical field can be trained on medical literature, case studies, and clinical guidelines, leading to more accurate medical advice and diagnoses.
  2. Improved Data Quality:
    • By curating high-quality, relevant, and representative training data, CustomGPT.AI can minimise the impact of biased or erroneous information.
    • Regular updates and audits of the training data can help maintain the model’s accuracy over time.
  3. Enhanced Contextual Understanding:
    • CustomGPT.AI models can incorporate advanced contextual understanding specific to the domain, which helps in generating more coherent and relevant responses.
    • This is particularly useful in complex fields where nuanced understanding is crucial, such as legal advice or financial analysis.
  4. Feedback Integration:
    • CustomGPT.AI can be designed to incorporate user feedback, allowing the model to learn from its mistakes and improve continuously.
    • This feedback loop helps identify and correct hallucinations, enhancing the overall reliability of the AI system.
  5. Combining with RAG (Retrieval-Augmented Generation):
    • CustomGPT.AI can be integrated with RAG to further reduce hallucinations. The retrieval mechanism can fetch accurate and up-to-date information, which the generative model can then use to produce reliable outputs.
    • This combination ensures that the generated content is grounded in factual data, reducing the chances of hallucinations.
  6. Citations:

Limitations

The main critical points on ChatGPT Limitations include [3]:

  1. Customization Restrictions:
    • OpenAI’s Custom GPTs offer limited customisation compared to platforms like CustomGPT.AI.
    • Users cannot fully control or modify the underlying model architecture.
  2. Data Privacy Concerns:
    • OpenAI’s data handling and privacy measures may not meet stringent business-specific security requirements.
  3. Cost Challenges:
    • High costs for usage, particularly in high-volume scenarios, can make OpenAI’s solution less suitable for budget-conscious projects or smaller businesses.
  4. Integration Limitations:
    • OpenAI’s platform lacks advanced integration tools, such as extensive API and SDK options, making it less developer-friendly for complex deployments.
  5. Restricted Analytics:
    • Basic analytics are offered, limiting the ability to deeply monitor and optimise GPT performance.
  6. Use Case Flexibility:
    • The platform is better suited for general applications and lacks the industry-specific focus and tools found in alternatives like CustomGPT.AI.

Implementation Strategies for CustomGPT

Implementation Strategies for CustomGPT.AI are the following:

  1. Fine-Tuning with Domain-Specific Data:
    • Collect and pre-process high-quality data relevant to the specific domain.
    • Fine-tune the GPT model using this data, ensuring it captures the nuances and specificities of the field.
  2. Regular Data Updates and Audits:
    • Establish a process for regularly updating the training data to include the latest information and remove outdated or incorrect data.
    • Conduct periodic audits to ensure the data remains accurate and representative.
  3. Incorporating User Feedback:
    • Develop mechanisms for users to provide feedback on the AI’s outputs.
    • Use this feedback to iteratively improve the model, addressing any identified hallucinations or inaccuracies.
  4. Integrating with RAG:
    • Implement a retrieval system to fetch relevant documents or information based on the input query.
    • Combine this retrieval system with the CustomGPT.AI model to enhance the accuracy and relevance of the generated responses.

Case Studies and Examples

MIT’s collaboration with CustomGPT.AI is a good example of ensuring AI accuracy by taking anti-hallucination measures seriously. MIT has built a priceless asset for entrepreneurs while preserving trust and credibility. This experience exemplifies the importance of accurate, reliable, trustworthy, and hallucination-free AI solutions. Read this case study in Lessons from the MIT Case Study: A Closer Look at AI Accuracy through Anti-Hallucination Measures.

There are more possible application examples as follows:

  1. Healthcare:
    • A CustomGPT.AI model trained on medical texts and clinical guidelines can provide more accurate medical advice, reducing the risk of incorrect diagnoses or treatment recommendations.
    • Integrating RAG can further ensure the model references the latest medical research and guidelines.
  2. Legal Services:
    • CustomGPT.AI models for legal applications can be trained on legal documents, case law, and statutes, improving the accuracy of legal advice and document drafting.
    • The RAG approach can help the model reference relevant legal precedents and statutes, reducing the likelihood of hallucinations.
  3. Customer Support:
    • CustomGPT.AI models tailored for specific industries can provide more accurate and relevant customer support, addressing queries with greater precision.
    • By integrating RAG, the model can retrieve the most pertinent information from a knowledge base, enhancing the reliability of the responses.

Conclusion

CustomGPT.AI offers a powerful approach to reducing AI hallucinations by leveraging domain-specific knowledge, high-quality data, and user feedback. When combined with RAG, CustomGPT.AI can further enhance the accuracy and relevance of AI-generated content, providing reliable and trustworthy outputs across various applications.

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References

1. A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

2. CustomGPT.AI

3. How to Build Your Own Custom GPT: A Comprehensive Guide to OpenAI and CustomGPT.ai

4. How To Stop ChatGPT From Making Things Up – The Hallucinations Problem

5. Context-Aware ChatGPT For Knowledge Management With Citations

6. Lessons from the MIT Case Study: A Closer Look at AI Accuracy through Anti-Hallucination Measures

7. LLMs Tag on daehnhardt.com

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About Elena

Elena, a PhD in Computer Science, simplifies AI concepts and helps you use machine learning.




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Elena Daehnhardt. (2025) 'How CustomGPT Mitigates AI Hallucinations', daehnhardt.com, 13 February 2025. Available at: https://daehnhardt.com/blog/2025/02/13/how-customgpt-minimises-ai-hallucinations/
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