Best Practice to Prevent Hallucinations

Why AI Hallucinations Happen

When you give an AI assistant a prompt, its goal is to generate a response that seems plausible based on the instructions. If the AI lacks the proper context and background knowledge, it may start "hallucinating" - making up information to fill in the gaps. Some common causes of AI hallucinations include:

• No relevant training data - If the AI simply hasn't been exposed to data from your domain, it won't have the proper knowledge to keep its responses grounded. Any specifics it generates are likely to be made up.
• Ambiguous or vague prompts - If your prompts don't provide enough constraints and details, the AI has more room for interpretation. This increases the chances of fabricated information.
• Asking about fictional contexts - Prompts that refer to imaginary products, people, or scenarios are prone to hallucinations since there are no facts for the AI to pull from.
• Insufficient contextual priming - Even if you provide some background info, the AI may hallucinate if it lacks the full context to understand your specific needs.

While AI hallucinations can be entertaining in some contexts, they have no place in business workflows where accuracy and trust are crucial.

The Best Practice of Grounding AI

The key to preventing hallucinations is grounding your AI by providing relevant data sources, reference materials, and background context. This gives the AI the knowledge it needs to generate accurate, tailored responses instead of making things up.

Some best practices for grounding your AI include:

• Upload relevant documents to InfoBase - Product specs, brand guidelines, writing samples, and other materials give the AI background knowledge to draw from.
• Pass data as inputs to workflows - Customer profiles, keywords, and other specifics prime the AI with details for each use case.
• Train AI assistants on company data - Exposure to real data improves the AI's domain knowledge over time.
• Tag InfoBase items - Makes it easy to reference relevant materials by topic.
• Continuously feed the AI real data - Ongoing learning prevents knowledge gaps that lead to hallucinations.
• Review outputs and provide feedback - Human guidance helps further improve the AI's knowledge.

The goal is to determine what information your AI needs to have on hand to generate accurate, high-quality responses tailored to each situation. With the proper grounding, the AI won't need to make things up - it can pull relevant facts and terminology directly from the provided data sources.

The Benefits of Grounding Your AI

Taking the time to properly ground your AI system pays off through:

• More accurate, factual outputs - The AI sticks to the specifics found in your data rather than fabricating information.
• Higher relevance and customization - Details from provided data improve personalization for different users and use cases.
• On-brand, compliant messaging - AI follows brand voice and guidelines based on supplied materials.
• Faster workflow creation - Less need to manually fix hallucinated outputs down the line.
• Greater user trust - Reliable, factual responses increase confidence in the AI.
• Reduced oversight needed - With quality grounding, minimal corrections to outputs should be required.

Grounding sets your AI up for success by giving it the knowledge resources it needs to provide useful insights tailored to each situation - all while avoiding distracting false information.

With proper grounding, you can minimize hallucinations and trust that your AI will incorporate real facts and details in its outputs. Invest time upfront in compiling training data, guidelines, and other grounding information to maximize the business value of your AI while avoiding frustrating inaccuracies.