Conquer AI hallucinations with these actionable strategies

AI is revolutionizing the business world and how!  

From streamlining operations and simplifying tasks to analyzing large data sets and enhancing decision-making, AI presents limitless opportunities to help you stay ahead of the curve.  

However, like every technology, AI has a downside, commonly called hallucination. This happens when AI models churn out false responses wrapped in such a confident, convincing tone that it's hard to differentiate between what’s true and what’s cooked up.  

AI hallucinations can be managed and controlled with the right strategies. 

By keeping humans in the loop, creating detailed prompts, and using RAG and guardrails, you can conquer hallucinations and harness the extraordinary power of AI. 

This blog will shed light on AI hallucinations, their causes, and the strategies you can adopt to manage and address them. 

Understanding AI hallucinations 

AI hallucinations happen when LLMs make up or imagine answers that seem plausible but are disconnected from facts or logical reasoning.  

But why the word “hallucination”?  

When humans hallucinate, they see, feel, and hear things that don’t exist, due to some underlying health conditions. Similarly, when AI misinterprets prompts due to multiple factors, it “hallucinates” the output.  

Hallucinations in AI can manifest in several forms from LLMs producing faulty texts to image recognition systems going wrong with image identification. 

Decoding the causes of AI hallucinations  

AI is definitely a game-changer if used right. But let’s not forget that it’s a machine, so everything it produces should be taken with a pinch of salt, especially when you whiff something suspicious in its response.  

Let’s find out why AI makes stuff up!  

Data is the DNA of AI models! 

The accuracy of AI predictions is directly linked to the quality of training data.  

A huge data set is fed into LLMs during training and these data sets might contain faulty, insufficient, or biased information. With such data as the base, AI models might churn out inaccurate or low-quality responses that steer away from facts.   

For example,  

⭐When a financial company’s AI model provides an overly optimistic market forecast based on outdated data, leading to poor business decisions and heavy losses. 

AI models tend to present incorrect answers confidently when faced with situations that they are not trained to deal with. This happens when the model’s data set is not diverse enough to represent all possible input types.  

Another reason for this can be a huge volume of irrelevant data, also called data noise. The AI system strictly applies what it has learned during training to the new data without understanding its context, resulting in outputs that are irrelevant and inaccurate.  

For example, 

⭐When a healthcare AI system trained exclusively on data from the western population, provides incorrect diagnosis and treatment plans when applied to patients from different demographics. 

Due to internal biases, the AI’s output accuracy drops for patients that fall outside of the dataset it’s trained on.  

AI being AI, it tries to provide an answer for everything, even if the prompt lacks clarity.  

Lacking the sense to cross-question and ask for more inputs for ambiguous prompts, AI models confidently spit out fabricated responses that can have serious consequences. 

While a generative AI model’s huge data set is a big benefit, there’s also a certain limitation to it. The data set, no matter how vast and diverse it is, can’t replace worldly knowledge.  Although LLMs can gain a good understanding through their datasets, they are not aware of how the real world works.  

So, when they are prompted to answer queries or provide information on new events, industry-specific areas, or complex concepts, that fall outside the scope of their training data, the responses can be erroneous or lack depth.  

For example,  

⭐ When tasked with screening candidates for the role of software developer, an AI tool might reject candidates if their profiles mention “Python developer”, instead of “software developer”.  

This happens because AI models rely on a limited dataset including specific keywords and patterns and lack a true grasp of how job roles and titles are phrased in the real world.  

8 Actionable strategies to overcome AI hallucinations  

The solution to hallucinations is right within your AI system.  

GenAI is incredibly powerful and adaptable, and with the right strategies, you can mitigate hallucinations and make the most of AI.  

With time, training data gets outdated and irrelevant, which is why it’s paramount to update your AI models regularly.  

This will leave little room for hallucinations as your AI model will be exposed to current data and trends. It will continue to learn from fresh real-world scenarios, sharpening its ability to generate optimal outputs that are free of flaws.  

Although AI is a pro at processing large volumes of data, its ability to understand contexts, ethics, and nuances, is still limited or even flawed sometimes. Keeping humans in the loop is therefore not a matter of choice, but a practice that should be an integral part of your AI-driven processes. 

With expert eyes reviewing and verifying the outputs, there is an essential, trustworthy layer of quality control that gives the assurance of high-quality, error-free responses. Human oversight can easily catch inaccuracies, allowing for timely intervention before users are impacted.  

As a best practice, you can establish clear-cut protocols for keeping human expert review as a mandatory step, especially in areas like finance, legal, and healthcare, before AI outputs are finalized or implemented.  

3. Engineer your prompts carefully 

With LLMs, the rule of thumb to get accurate responses is to make your prompts as specific and precise as possible.  

Remember one thing with prompt engineering – never beat around the bush. Keep the instructions simple with clear contextual cues.  

Be direct about what you need and what you don’t. This will reduce ambiguity and guide the AI model to focus on specific tasks and prioritize the instructions mentioned in the prompt. You can conduct training for your teams to help them create a repository of high-quality prompts that will come in handy for keeping ChatGPT hallucinations at bay.  

You can also introduce them to the different prompting techniques they can implement when using LLMs. Some of these are:  

• Chain-of-Thought prompting
  Break complex problems into small, easy-to-understand pieces and enable AI to follow a logical sequence and provide well-aligned responses.  

• Zero-shot prompting
  Create a detailed prompt assuming that the LLM has no prior knowledge about the subject. 

• One-shot prompting
  Provide an example along with your prompt to help AI understand the context better.  
  

• Few-shot prompting
  Give a few examples to aid AI in understanding the style or format of responses you need. 

• Iterative prompting 
  Keep fine-tuning your prompts, guiding AI to reach the desired answer.  

• Negative prompting
  Clearly specify what you don’t want the AI to do or include in its output, ensuring that the results are aligned with your needs.  

Based on your queries, you can select the right prompting technique and tailor AI’s responses to your requirements. 

4. Embrace RAG to reduce hallucinations 

Retrieval Augmented Generation (RAG) empowers LLMs to pull data from a vast external information repository, making your AI interactions precise and accurate. With the capability to browse through external pages, LLMs are able to provide responses that are current, informative, and context-relevant.   

Once the information is retrieved, it is pre-processed and integrated into the LLM. This equips LLMs with much more context about the topic, enhancing their understanding of the queries.   

Here’s why RAG-based LLMs should be your top choice to reduce hallucinations:   

• Access to current information 
  With RAG integration, LLMs have access to current information that enables them to produce outputs with the most up-to-date information.   

For example,   

⭐Lawyers or even in-house counsels could use RAG to enable AI systems to look for the latest cases, laws, and regulations, before generating, reviewing, and analyzing contracts. This ensures that the output is in sync with the current legal standards.  

• Cite sources to users
  RAG also enables LLMs to cite the sources they use to collect information, making way for human verification.  

• Makes LLMs more versatile
  With access to a vast external knowledge source, LLMs become efficient at handling a wide range of prompts, without the need to cook up false answers.   

Implementing Guardrails is another winning strategy you can adopt to reduce AI hallucinations.  

Guardrails set boundaries for AI models using pre-defined rules and constraints. These rules act as safety nets, ensuring that LLMs operate within the defined scope and don’t stray away from the facts.  

These can be in the form of:  

• Technical controls that are incorporated right into the AI system
• Policy-based guardrails that involve guidelines, best practices, and frameworks providing the right direction to LLMs 
• Legal guardrails that ensure that the latest laws and regulations are considered when producing the output 

For example,  
Guardrails in LLMs used by healthcare companies ensure that HIPAA regulations are followed when working on patient data. 

With guardrails in place, your AI models will provide more fact-based, reliable responses that are within regulatory guidelines, helping you make better business decisions. 

One of the best ways to deviate your AI model from hallucinating is by equipping it with a structured data template.  

Data templates are great at setting boundaries and helping AI stay focused on the task at hand. Using templates, you can provide AI models with a defined format, allowing them to function in a controlled and structured environment.  

Data templates leave little scope for confusion and empower AI to work with full clarity and stay away from misinterpretations. This strategy is best for cases where you need a standardized output, such as reporting and data entry. 

For example,  

To analyze a product page, you can instruct the AI tool to extract appropriate information and fill in the template. You can also ask it to not include information that’s not mentioned in the page.  

The template could be something like this:  

For each of the headers, you could add a specific instruction to prevent the AI tool from deviating from the task.  

7. Set the right temperature to control the degree of randomness in AI responses 

It’s possible to govern the degree of randomness or ambiguity in AI responses through a feature called “temperature”. Using this feature, you can govern how focused or how random and creative you want the AI output to be.  

While a lower temperature leads to more focused responses, a higher temperature results in creative, but sometimes, hallucinated outputs.  

If you keep a lower range, your AI system will be less prone to hallucinated, unpredictable responses. This will also make sure that AI outputs prioritize accuracy over creativity and are well-aligned with the inputs you provide.  

So, if it’s the business-critical operations in question, it’s good to be on the lower end of the range. However, if you need assistance for tasks like brainstorming or content generation, a higher temperature will pave the way for more creative and varied outputs.  

When it comes to AI models, security and trustworthiness should be at the top of your priority list when selecting an LLM. Going for a trusted and reputed LLM ensures that your AI system is reliable, rigorously tested, and less prone to hallucinations.  

While there are several LLM providers out there painting a positive picture of how their AI models can solve all your business problems, in reality, there are only a few platforms that score high on accuracy, security, and compliance.  

It’s best to be on the safer side and opt for LLMs from renowned players like OpenAI, Microsoft, and Google. Since these companies have invested heavily in research, testing, AI governance and management, the chances of hallucinations are lesser as compared to LLM providers who are new to the market.  

You can also choose a few AI players and evaluate them based on their performance history, user reviews, and commitment to security, before making the final call. 

Hallucinations could soon be a challenge of the past

Recent research by the scientific journal Nature reveals an exciting breakthrough: scientists have come up with an advanced method for detecting confabulations – a subset of AI hallucinations. These are plausible-sounding outputs that are often disconnected from reality and can easily manipulate users.  

The new method allows users to identify when a prompt is likely to generate a confabulation, helping them take extra care and opening new possibilities when using LLMs.  

And there is more good news!  

Open AI has introduced a new safety feature, CoT (chain of thought) in its latest model – o1. This innovative approach enables real-time double-checking of responses, ensuring higher accuracy and reliability.  

This technique works in a simple way.  

Don’t ask your AI model for a direct answer to a complicated problem. Ask it to break down the problem into smaller steps and then come to the solution. While this will give you detailed insights into the matter, it will also let AI produce reliable responses that are right on target.  

Also, let’s not forget that when it comes to AI, we’ve just scratched the surface so far. AI holds vast potential to redefine innovation by opening possibilities we can only imagine today. As AI models advance, new features and techniques will emerge, and hallucinations may soon become a challenge of the past. 

How is Google addressing AI hallucinations?  

⭐ Remember the rage Google’s much-hyped AI tool Bard’s (now Gemini) initial public demo created back in February 2023?  

According to The Verge, Bard confidently provided a factually incorrect response, when asked - “What new discoveries from the James Webb Space Telescope can I tell my 9-year-old about?” 

Since this incident, Google has been on a spree, refining its AI models through ongoing research and development. 

Google has come up with Data Gemma, an open model that connects LLMs with real-world data extracted from Data Commons – a centralized, constantly expanding repository of reliable, publicly available data.  

Data Commons sources data from well-known organizations like the U.N. and W.H.O., making it a highly trustworthy platform for information gathering.  

This integration is based on 2 unique approaches:  

1. RIG (Retrieval-Interleaved Generation) enables the LLM to fact-check information against Data Commons, before generating a response.  

Since these developments, Google has observed higher accuracy in its LLMs and is confident that users will experience fewer hallucinations going forward.   

The company shared: 

“Our research is ongoing, and we’re committed to refining these methodologies further as we scale up this work, subject it to rigorous testing, and ultimately integrate this enhanced functionality into both Gemma and Gemini models, initially through a phased, limited-access approach.” 

With the rapid pace at which research is going on, hallucinations might soon become a rare occurrence, as these technologies improve and evolve. 

Are AI hallucinations a concern for businesses?  

While AI offers immense potential, hallucinations, if left unchecked, can pose risks to various areas of your business.  

From compliance challenges to decision-making errors and financial losses, hallucinated outputs tend to steer you away from the right direction, triggering actions that can have a profound impact on multiple business aspects.  

For sectors like Finance, for instance, timely and accurate financial disclosures are required to meet SEC regulations. AI hallucinations producing flawed financial data can result in erroneous reporting, which may result in compliance issues.  

Similarly, hallucinations by customer support chatbots might compromise data privacy, potentially resulting in GDPR violations and costly lawsuits from affected users. Decision-making is another area where misleading outputs turn out risky, affecting product launches, resource allocation, or budget management.  

For example,  

⭐ An AI system might provide an overly optimistic view of a particular market, leading to poor marketing decisions, and affecting the product launch and all related activities. 

Another major reason why AI hallucinations are a cause of concern is the financial risk they pose to organizations.  

Financial losses can manifest as costly lawsuits, penalties, wrong hiring, or incorrect forecasts that might impact your bottom line.  

For example,  

⭐ An AI platform incorrectly allocating ad spend based on a flawed understanding of customer behavior, could drain the marketing budget, without producing any tangible returns. 

Is there a creative side to AI hallucinations?

Yes, there is a surprising upside to hallucinations!  

While most of the time AI hallucinations are unwanted, there are some use cases where they can be a source of creative inspiration.  

Fields such as creative writing and storytelling, art and design, advertising, and music composition, require out-of-the-box thinking, unexpected twists and turns, and that’s exactly where hallucinations can prove powerful.  

A new character or situation might pop up from an AI-generated script, giving an entirely different turn to the story!  

An AI-powered graphic design tool might come up with a never-thought-before combination of colors and designs, pushing the creativity level of designers! 

An advertiser can use tools like ChatGPT and Bing to get bold, interesting, offbeat taglines for a new product line!  

The tendency of hallucinations to disrupt the conventional and churn out unexpected ideas can lead to new discoveries that were not possible before, even in areas like sales, and HR, where things usually go the traditional way.  

For example,  

AI hallucinations can introduce sales teams to an unusual target audience or unexpected product bundles, helping them create ingenious sales strategies to tap new markets and push sales.  

Such hallucinations have the power to fuel your imagination, so you can infuse creativity into projects and build something magical. There is no doubt that hallucinations can be risky for certain business areas, but they also carry within them opportunities that never existed before.  

Handle hallucinations with practical strategies and an open mind 

While LLMs might have a great memory and faster data processing power, they come with their own set of drawbacks – hallucinations being the major one.  

But the good thing is that you can control hallucinations to a great extent by doing simple things right, such as being super careful when writing prompts, harnessing RAG and guardrails, and using data templates to achieve reliable responses. 

Not to forget, keeping humans in the loop is non-negotiable as it ensures that AI-generated responses are thoroughly checked before implementation.  

Lastly, keep an open mind when dealing with hallucinations, as these act as double-edged swords that are harmful but can be equally beneficial in business areas or fields that value creativity more than anything else.