In recent years, Generative Artificial Intelligence (AI) has evolved from 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, discussed how this technology is used to offer tailored solutions to companies.
However, one of the biggest challenges this technology faces is the well-known “hallucinations,” where AI models generate responses that, although 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?
LHallucinations occur when an AI model, while generating content or making predictions, deviates from reality and presents false or nonsensical information. This can happen because the AI learns from the data it is provided; if that data is biased, outdated, or poorly labeled, the AI internalizes those imperfections and reflects them in its output.
In critical sectors such as finance, healthcare, or customer service, hallucinations can have serious consequences, ranging from loss of user trust to business decisions based on incorrect information. Therefore, ensuring data quality is essential to prevent these types of errors.
Data quality: The first line of defense against AI hallucinations
Data management is fundamental to prevent hallucinations from affecting generative AI projects. AI depends on enormous volumes of data to learn and generate content. If that data contains biases, errors, or duplications, the model is negatively affected. An effective data strategy focuses on ensuring that the information the AI works with is:
- Accurate: Data must be up-to-date and well-documented so the model doesn’t generate outdated or incorrect information.
- Diverse: Data variety is key to avoiding the model becoming biased toward a single perspective.
- Correctly labeled: Proper data classification helps the model interpret the information accurately.
Strategies to ensure data quality
A strong data strategy not only prevents hallucinations but also improves the AI’s ability to generate truthful and useful responses. This is crucial in sectors such as technology or financial services, where a small inaccuracy can translate into financial losses or a poor customer experience.
To prevent hallucinations, companies must adopt key practices in their data strategy:
- Regular audits: Continuously review data sets to identify potential errors, duplications, or outdated information.
- Data cleaning processes: Implement data preprocessing tools that identify and remove any outliers 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 that invest in solid data infrastructure and strategies will not only avoid hallucinations but will also optimize their processes, improve model accuracy, and deliver more personalized and precise experiences to their customers.
A tangible example of how proper data management can transform a business is Santalucía, one of the leading insurance companies in Spain. In collaboration with PUE, Santalucía implemented a generative AI-based solution that reduced customer service query times by 85%, from 90 seconds to just 13.
With its expertise in managing Datalakes and its focus on DataQuality, PUE continues to be a leader in helping companies navigate the challenges of generative AI, ensuring that every step is grounded in reliable data.
