How We Effectively Reduce AI Hallucinations in Firstscore Platform

How We Effectively Reduce AI Hallucinations in Firstscore Platform

In the Firstscore platform, we apply several straightforward methods (plus two particularly demanding ones) that effectively reduce AI hallucinations. Here they are:

1. Tuning Identity – AI must "know" what it is and what it isn’t.
2. Establishing Context – Without it, the model will make its own assumptions.
3. Setting Boundaries – Like in raising a child, AI should "know" its scope of operation and stay within it.
4. Limiting Language Flexibility – The more precise the style, the fewer surprises.
5. Focusing on the Problem – The model should avoid unnecessary digressions.
6. Relying on Facts – AI should first and foremost rely on the provided information, i.e., the data.
7. Introducing a Supervisor – Automatic conversation monitoring, where one agent oversees another.

The hardest point is the one regarding facts – providing hard data requires collecting, processing, and quickly feeding it to the model. This is the type of work that commercial model creators perform.

In our case, most of this data consists of private, unique corporate information, unavailable on the internet, or carefully selected industry insights that, without proper handling, get lost in the sea of information. After proper processing, the algorithms become more valuable than standard market solutions.

Although, as we know, most AI implementations today are in training and AI use in companies is considered complex, relying on data, we can quickly achieve valuable and efficient solutions.

The future lies in the data-centric AI approach and niche solutions. Forbes is already writing about it, and I fully agree with that.

Author: Mariusz Jażdżyk

Lecturer at Kozminski University, author of the book “Chief Data Officer,” specializing in building data-driven organizations. He supports startups in the practical implementation of data strategies and AI solutions.