How to Minimize Hallucinations in AI Models with Effective Prompts

AI models can generate false or inaccurate information. They can generate outputs that are imaginative but lack a grounding in reality. “Even state-of-the-art models are prone to producing falsehoods —they exhibit a tendency to invent facts in moments of uncertainty,” according to researchers at OpenAI who built the ChatGPT platform.

Major reasons for AI Hallucinations include insufficient or biased training data, ambiguity in input prompts or too much training on specific datasets. Hallucinations impact the reliability of AI-generated content and raise concerns about potential misuse in sensitive fields like medicine, finance law, and cybersecurity.

The hallucination phenomenon is still not well understood by researchers investigating mitigation strategies. One strategy being explored by OpenAI researchers is to train AI models to reward themselves for each individual, correct step of reasoning when they are arriving at an answer, instead of just rewarding a correct final conclusion. This strategy encourages models to follow more of a human-like chain of “thought” approach to minimize the chances of generating fabricated content.

We are far from an acceptable solution as AI Models are currently not particularly good at solving challenging reasoning problems. However, the prompting best practices outlined below can help in reducing the risk of hallucinations in the AI generated content:

1. Provide Clear Context: Clearly define the scope, context, and purpose of the prompt. Specify any constraints, guidelines, or desired outcomes. The more specific and well-defined the prompt is, the better the chances of receiving relevant and accurate responses.

Prompt: “You are an AI language model designed to assist in recipe recommendations. Given the context of a vegetarian diet, suggest a flavorful and protein-rich dinner recipe that incorporates seasonal vegetables.”

In this example, the prompt sets clear context by specifying the AI’s purpose, which is to recommend recipes. It further narrows down the context by mentioning a vegetarian diet, indicating dietary restriction. Additionally, it provides specific criteria for the recommendation, such as the need for a flavorful and protein-rich dinner recipe that incorporates seasonal vegetables.

By providing clear context in the prompt, the AI system understands its role, the target audience (those following a vegetarian diet), and the requirements for the recommended recipe. This clarity helps minimize the chances of generating irrelevant or non-compliant responses, focusing the AI’s output on providing suitable recipe suggestions within the given context.

2. Request Factual Information: If you require factual information in the response, explicitly ask for it. Encourage the AI system to provide evidence, sources, or data to support its answers. This helps ground the response in reality and reduces the likelihood of hallucinations.

Prompt: “You are an AI language model with knowledge of various scientific disciplines. Explain the concept of gene editing using CRISPR technology and discuss its potential applications in medicine and agriculture.”

In this prompt, the AI system is informed about its training in various scientific disciplines. It asks the AI to provide an explanation of the concept of gene editing using CRISPR technology, a revolutionary gene-editing tool. Additionally, it prompts the AI to discuss the potential applications of CRISPR in medicine and agriculture.

By leveraging a pre-trained model or template specifically trained on scientific topics and advancements, you can tap into the AI’s knowledge of gene editing and its potential applications. This ensures that the generated response is accurate, informative, and grounded in scientific understanding, reducing the likelihood of generating misleading or speculative information.

3. Set Logical Constraints: Introduce logical constraints in the prompt to ensure that the generated response follows reasonable and coherent reasoning. Specify any limitations or boundaries that the AI system should consider when generating its response.

Prompt: “You are an AI language model trained to assist with historical inquiries. Please provide a response based on verified historical facts and avoid speculative or fictional content. What were the major causes of the American Civil War?”

By explicitly mentioning the need for verified historical facts and avoiding speculation or fiction in the prompt, you establish a logical constraint. This helps guide the AI system to provide an accurate and grounded response based on historical evidence and scholarly research, minimizing the chances of hallucinations or the generation of fictional narratives.

4. Include Multi-turn Prompts: Break down complex questions or tasks into multiple prompts, allowing the AI system to provide step-by-step responses. This approach helps to ensure that the generated answers remain focused and coherent throughout the conversation. Let us learn more about multi-turn prompts with an illustrative example:

User: “How can I make healthier food choices when dining out? Outline a step-by-step approach.

AI: “Making healthier food choices while dining out is a common concern, even in the nutrition segment. Let us explore some strategies through a multi-turn prompt:

Step 1: Review the Menu:

When dining out, take the time to review the menu in advance. Look for healthier options, such as dishes with lean proteins, vegetables, whole grains, and minimal added sugars or unhealthy fats.

User: “According to nutrition experts, why is reviewing the menu important for making healthier food choices?”

AI: “Great question! Reviewing the menu allows you to assess the available options and make informed decisions. Nutrition experts often recommend scanning the menu for healthier choices to ensure you select meals that align with your dietary goals and preferences.”

Step 2: Ask for Modifications:

Do not hesitate to ask for modifications to meet your specific dietary needs. Requesting substitutions or alterations, such as opting for steamed vegetables instead of fried sides or requesting sauces and dressings on the side, can significantly impact the nutritional profile of your meal.

User: “How does asking for modifications contribute to making healthier food choices?”

AI: “Asking for modifications empowers you to customize your meal to suit your dietary preferences and goals. By requesting changes, you can reduce the intake of unhealthy ingredients like excessive salt, added sugars, or unhealthy fats, and instead opt for options that are more aligned with your health objectives.”

Step 3: Portion Control:

Practice portion control by being mindful of serving sizes. Restaurants often serve larger portions, which can lead to overeating. Consider sharing a dish with a dining partner, ordering appetizers as a main course, or taking leftovers home for later.

User: “What are the benefits of practicing portion control when dining out?”

AI: “Practicing portion control can help manage calorie intake and promote healthier eating habits. By being mindful of serving sizes, you can avoid overeating and maintain better control over your calorie consumption, supporting weight management and overall health.”

By following these steps – reviewing the menu, asking for modifications, and practicing portion control – you can make healthier food choices when dining out in the nutrition segment and enjoy meals that align with your dietary goals.”

5. Test and Iterate: Experiment with different prompts and evaluate the quality and reliability of the AI-generated responses. Iterate and refine your prompts based on the system’s performance, adjusting the wording or structure as necessary to elicit accurate and contextually appropriate answers.

6. Incorporate Human Review: When dealing with critical or sensitive topics, consider involving human reviewers to validate the generated responses. Human oversight can help identify and correct any potential hallucinations or inaccuracies, ensuring the reliability and trustworthiness of the information.

7. Use Pre-trained Models or Templates: Leverage pre-trained models or templates designed for specific tasks or domains. These models have been fine-tuned and validated, reducing the chances of hallucinations and enhancing the accuracy and relevance of the generated responses.

Good examples of pre-trained models are BERT which has a strong understanding of language and semantics and GPT 3/4  which has vast knowledge and language generation capabilities. In the future expect newer pre-trained models for vertical industry segments like medical and legal fields.

8. Regularly Update and Retrain Models: Stay updated with the latest advancements in AI technologies. Regularly update and retrain AI models with diverse and representative data to enhance their understanding of the real world, mitigate biases, and improve response accuracy.

While these practices can help minimize hallucinations, it is important to remain vigilant and critically evaluate the output of AI systems. Continuously monitor and verify the generated responses, ensuring they align with factual information and logical reasoning.

In conclusion, by following the above best practice recommendations, we can strike a balance between encouraging AI system creativity and ensuring reliable, accurate, and contextually appropriate responses, minimizing the risk of hallucinations in AI-generated content.