Discover how to avoid hallucinations in Generative AI and improve outcomes with a solid data strategy
Discover how to avoid hallucinations in Generative AI and improve outcomes with a solid data strategy
In recent years, Generative Artificial Intelligence (AI) has evolved from being an emerging technology to a key pillar in many sectors. In a recent interview with Muy Computer Pro, Sergio Rodriguez de Guzmán, CTO of PUE, discusses how the use of this technology provides tailored solutions for businesses.
However, one of the greatest challenges facing this technology is the well-known "hallucinations," where AI models generate responses that, while seemingly coherent, are incorrect or inaccurate. This issue is closely related to the quality of the data used in AI models.
What are hallucinations in Generative AI? Hallucinations occur when an AI model, while generating content or making predictions, deviates from reality and presents false or nonsensical information. This can happen because AI learns from the data provided to it; if that data is biased, outdated, or poorly labeled, the AI internalizes those imperfections and reflects them in its results.
In critical sectors such as finance, healthcare, or customer service, hallucinations can have serious consequences, ranging from a loss of user trust to business decisions based on incorrect information. Therefore, ensuring data quality is crucial to prevent such errors.
Data quality: the first line of defense against AI hallucinations Data management is key to preventing hallucinations in generative AI from impacting business projects. AI relies on massive volumes of data to learn and generate content. If that data contains biases, errors, or duplications, the model will be negatively affected. An effective data strategy focuses on ensuring that the information the AI works with is:
Accurate: The data must be up-to-date and well-documented to prevent the model from generating outdated or incorrect information.
Diverse: Variety in data is crucial to prevent the model from becoming biased toward a single perspective.
Properly labeled: Proper data classification helps the model interpret information correctly.
Strategies to ensure data quality A good data strategy not only prevents hallucinations but also enhances AI’s ability to generate truthful and useful responses. This is crucial in sectors like technology or financial services, where even a small inaccuracy can result in financial losses or a poor customer experience.
To prevent hallucinations, companies must adopt key practices in their data strategy:
Regular audits: Continuously review datasets to identify potential errors, duplications, or outdated information.
Data cleaning processes: Implement data preprocessing tools that identify and remove any atypical or incorrect information before it reaches the AI model.
Cross-validation: Use independent test data to ensure the model continues to generate accurate and reliable responses.
As generative AI becomes an indispensable tool for businesses, proper data management will be the key to success. Companies investing in solid data infrastructure and strategies will not only avoid hallucinations but also optimize their processes, enhance the accuracy of their models, and offer more personalized and precise experiences to their customers.
A tangible example of how proper data management can transform a business can be seen in Santalucía, one of Spain’s leading insurance companies. With the collaboration of PUE, Santalucía implemented a solution based on generative AI to reduce customer service agents’ query times by 85%, from 90 seconds to just 13.
PUEDATA, with its expertise in managing Datalakes and focus on Data Quality, continues to be a leader in helping businesses navigate the challenges of generative AI, ensuring that every step is grounded in reliable data.