Every relationship requires certain compromises, and AI is no exception. After the initial fascination with LLM models, we began to notice their flaws. What is the biggest fault of GenAI? I think we can all agree here: the so-called AI hallucination.

Hallucinating is part of the nature of generative models, but does that mean we have to accept it when implementing them for our own needs? Absolutely not — there are ways to deal with this unpleasant issue. To handle it effectively, it’s worth first understanding exactly where the phenomenon of hallucination comes from. Know your enemy — there’s no better way!

What is AI hallucination?

Let’s begin by clarifying what AI hallucination actually is. Yes — it may have much in common with human hallucination, which is seeing things where they don’t exist. A simple error in data description can cause generative artificial intelligence to misinterpret facts, for example, seeing monkeys as cats or spoons in teacups.

More often, however, AI hallucination takes other forms. A detailed, substantive answer to a question that doesn’t make sense: for example, how long it takes to travel from one end of the Earth to the other. A very convincing but completely false piece of information woven into the response. 

Generative artificial intelligence sometimes simply makes things up instead of admitting it doesn’t know. It also cannot detect manipulation, which can result in such errors as the one mentioned above. What causes this? The answer lies in the design and nature of LLM training.

Why do LLM models hallucinate?

The key to understanding the nature of hallucination is realizing that LLMs generate text by predicting the next word in a sequence based on patterns learned during training. They are not fact-based retrieval systems but probability-based models. 

When presented with a query, especially if it lacks clear context or references, the model may “fill in the gaps” by generating plausible but incorrect information. This process is called hallucination. Why does it happen?

LLMs lack awareness and reasoning

They don’t “understand” facts the way humans do. Instead, they process relationships between words and phrases, so the generated output can sometimes seem factual when it is really a best guess or imaginative extension.

Note: this might soon change. Although the popular LLMs rely on borrowed logic, Open AI is putting a lot of effort into developing reasoning. Its freshly released model, Strawberry, is a huge step ahead in that area.

LLMs lack grounded knowledge

Although trained on massive datasets, LLMs don’t have a direct connection to a factual database or real-time knowledge. Once their training is complete, they don’t “know” what is real or not—they only approximate based on the text they’ve seen. This can lead to errors, especially if they encounter an unfamiliar topic or try to generate highly specific details that weren’t present in the training data.

LLMs are trained with noisy data

Training data comes from the internet, books, and various other sources, which may contain errors, inaccuracies, or fictional information. When models are trained on such data, they might inadvertently generate incorrect information or even repeat inaccuracies from their training set.

LLMs are designed to produce creative and coherent text

This creativity sometimes leads to generating information that seems plausible but is untrue, especially when they are asked about things not found in their training data or when extrapolating information.

Why AI is unpredictable? The probabilistic nature of LLMs

Imagine asking an LLM: “Can you recommend a good book for a weekend read?” On one occasion, it might respond with a classic novel, “Pride and Prejudice.”. On another, it could suggest a modern thriller, like “Gone Girl.”

Why do its answer change despite the prompt being formulated in exactly the same way? LLMs, like GPT, are unpredictable mainly due to their probabilistic nature and the limitations of the data they are trained on. During training, they rely on random processes such as random initialization and random sampling of data, which means that even with the same data, they can learn different representations.

LLM’s unpredictability and context dependence

The model’s responses are generated based on the context, but if that context is ambiguous or too long, the model may miss important information, leading to incorrect or inconsistent answers. For example, if you ask something vaguely, the model might generate different, sometimes contradictory, responses because it predicts words based on a limited fragment of text.

Additionally, the models generate responses probabilistically, meaning the same request can lead to different results each time. They use techniques like sampling, which introduce randomness to create more creative responses, but also increase the risk of errors. For instance, if you ask the model for advice, it might give a correct answer the first time, but the second time, it could provide something inaccurate.

Fight LLM’s Unpredictability by Controlling Model’s Output with Parameters

To control the model’s creativity or variability in responses, you can adjust key parameters like temperature, top-k, top-p, and max-output. These parameters allow you to fine-tune the balance between predictable and diverse answers:

• Temperature controls randomness—lower values yield more deterministic answers, while higher values result in more varied, creative outputs.
• Top-k limits the number of next-word candidates considered, ensuring more focused answers.
• Top-p (nucleus sampling) adjusts the probability threshold for word selection, controlling the diversity of responses.
• Max-output defines the maximum length of the generated response, ensuring concise or extensive outputs as needed.

How to prevent AI hallucination and unpredictability?

Formulate prompts in a very precise manner. Provide the model with extensive context. Break down the task into smaller parts, so the model can better understand what you mean. You can find plenty of similar advice in social media and the growing number of courses aimed at preventing hallucinations. 

However, these tips are designed strictly for personal use. They don’t apply to businesses looking to integrate GenAI into their solutions. When it comes to customer interactions with GenAI assistants, especially those integrated with models like OpenAI, the approach should be slightly different. Here’s what you should focus on to ensure accurate responses:

Integrate with a Knowledge Base

To minimize the risk of hallucinations, connect your AI model to an external knowledge base or search engine. This allows the model to access up-to-date, factual information instead of relying solely on predicting answers.

Add a Verification Layer

Implement an additional logic layer, such as reasoning systems or expert rules, to monitor and verify the model’s generated responses. This serves as extra protection against incorrect or imprecise content.

Introduce a Fact-Checking Module

If accuracy is your priority, link the LLM with a fact-checking tool. This will provide an additional filter to verify the correctness of generated content, reducing the risk of hallucinations.

Coordinate the Prompts

Design the assistant’s interface and logic  in such a way that clients have access to suggestions and ready-made, common cases. This way, you have greater control over the quality of the prompts.

Educate your Users About AI Hallucination

You can also take the initiative and actively familiarize users with your solution. Educate them on how to formulate queries and address any doubts in your communication on social media profiles. If any “slip-ups” occur, don’t ignore them. Approach them with humor and focus on education.

Fine-Tune the Model

Fine-tuning involves training the model on domain-specific data to improve its performance for specialized tasks. If your business operates in a highly specialized industry, consider fine-tuning the GenAI model with proprietary or industry-specific datasets. This enhances the model’s understanding and reduces the likelihood of hallucinations by grounding its outputs in verified, context-rich data.

Leverage Zero-Shot Inference to Fight AI hallucination

Zero-shot inference refers to the model’s ability to handle tasks it wasn’t explicitly trained on. By providing clear and specific prompts, you can tap into this ability for a wide range of tasks without requiring further training. For instance, instead of asking the model to simply “summarize,” specify the tone, length, or details needed. Zero-shot inference can save time and resources by enabling the model to generalize across various topics.

RAG – your best weapon against AI hallucination

RAG, or Retrieval-Augmented Generation, is a game-changer when it comes to stopping AI hallucinations. Why? Because instead of just guessing based on what it learned during training, RAG allows the AI to pull in real, up-to-date information from external sources while it’s generating a response. This means it’s not just making things up when it doesn’t know something—it can check facts on the fly. 

So, if you’re worried about your AI going off-script or giving incorrect answers, RAG helps keep things accurate and grounded in reality. It’s like giving your AI a reliable fact-checker.

AI hallucination and unpredictability: Why it’s not your problem?

When implementing GenAI features integrated with open LLMs through us, you can immediately enhance them with mechanisms that minimize the likelihood of hallucinations. With RAG, every piece of information will be cross-checked against your knowledge base, which contains verified data about your products, services, policies, etc. Whether it’s for an assistant or content generation, this approach helps ensure the accuracy and correctness of the returned information.

Also, keep in mind that hallucinations typically occur with very specific questions when interacting with assistants. For routine customer service, the vast majority of inquiries are repetitive and leave little room for errors.

At G-Group, we’ve mastered this through the series of GenAI implementations. We’ll help you minimize the risk of hallucinations and maximize your project’s chances of success.