Jobe.ai

AI hallucinations – the tendency for the AI to ‘make stuff up’ – is an ongoing issues stopping its widespread use for many applications.

We found this troublesome in creating AI-authored courses, answering questions, AI enabled testing, among other uses.

Some practical strategies we found that have worked include:

➡ Using the latest and greatest models: There are experts working on this issue daily, stand on the back of their work.
➡ Fact Checkers: Create fact-checker AI Agents, with access to the internet, your documentation, and source materials. Ensure easy references are available to the applicable source materials.
➡ AI Agents teams: Specialized team of AI agents, that discuss and challenge the outputs.
➡ Code functions: Don’t only rely on the LLM. Traditional code functions can be made to do 100% accuracy checks in critical areas.
➡ Human Touch Points: Make sure you include “human touch points” in the flow – places where information can be displayed, and decisions made by humans.

Using all these strategies might seem expensive today, but long-term cost savings are enormous.

What strategies have you used to improve the accuracy of AI?

(100% human written – Ai Image)