Why LLM Fine-Tuning Isn’t Always the Best Path
Why LLM Fine-Tuning Isn’t Always the Best Path
Training LLMs on Company Data

AI Hallucinations: Why They Happen and How to Stop Them

Artificial Intelligence (AI) has now become an industry-disrupting technology in a few industries, such as healthcare, finance, and customer service. Nowadays, the business industry employs AI solutions for automation, business intelligence, and customer solutions. But even with its advancement, AI faces a puzzling issue: the problem of hallucination.

AI hallucinations are when an AI system has produced incorrect or somewhat misleading pieces of information and poses them as facts. Such happenings can be hilarious in ordinary chat but dangerous in serious situations like consultations in law, medicine, or finance. AI's reliability will be affected by understanding how it makes and distinguishes hallucinative reasoning .
How AI Develops Hallucinations
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At its very core, AI identifies patterns within massive datasets and generates an output based on probabilistic models. Unfortunately, this process has certain drawbacks. When AI encounters gaps in knowledge or ambiguous input, it attempts to fill in the blanks; sometimes incorrectly. Unlike people, AI doesn't get things or have gut feelings; it just guesses the words that come next based on what it learned before.
Hallucinations can cause due to several factors:
  • Data limitations: AI models are only as good as the data used to train them. If the dataset is incomplete or carries some bias, AI can generate misleading responses.
  • Overgeneralization: AI has a tendency to continue from the past pattern even when little information is available which results in made-up outputs.
  • Lack of Real-Time Verification: Unlike humans who can fact-check, AI lacks built-in mechanisms to verify the accuracy of its responses dynamically.
Grounding AI: Keeping It Real
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This would imply trying to mitigate hallucinations through a systematic process that will ensure the responses given by AI are based on verifiable information. This process is known as grounding, which links AI-generated outputs to reliable data sources.
1. Retrieval-Augmented Generation (RAG)
RAG enables the AI models to be improved such that they can locate real-time, relevant information from some trusted source before shaping their answers. This technique lowers the chances of hallucination since it allows for the integration of checked and external knowledge instead of singularly relying on pre-trained data.
How RAG works:
  • The AI receives a query.
  • It retrieves relevant information from curated databases.
  • It synthesizes a response based on factual data.
By incorporating external verification, RAG helps AI remain accurate, preventing it from "making things up."
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2. Prompt Engineering- The Ability of Asking the Right Questions
By incorporating external verification, RAG helps AI remain accurate, preventing it from "making things up."
Best practices for prompt engineering:
  • Be Specific: Ambiguous prompts lead to uncertain outputs. Instead of "What's the return policy?", ask something like, "Please provide the return policy according to the official company website."
  • Provide Context: Providing background information will help the AI be more precise in its answer. For example, "According to our 2024 company handbook." This should help make it more accurate.
  • Set Constraints: Limiting AI's response scope prevents speculation. Direct instructions like, "If you don't have a verified answer, state that the information is unavailable" help avoid misinformation.
  • Fallback mechanisms: AI should realize when to tune back its response because it does not have adequate data to generate a response. It should either ask for more context or indicate the input of a human being.
The Business Impact of Grounding AI
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Assuring the reliability of AI is no longer a technical necessity but rather a business imperative. Untrustworthy outputs from AI can undermine consumer trust, damage brand reputation, and create the potential for costly legal ramifications. Organizations committing to implement grounding techniques will be rewarded by AI that is not only intelligent but also reliable.

Studies show that 65% of consumers tend to trust organizations that are transparent about the use of AI. By rolling out robust grounding methods, the companies can build customer assurance and command the moral leadership of AI deployment.
Looking Ahead: The Future of AI Accuracy
Importance of Training LLM and Company Data
Though AI hallucinations could not vanish overnight, changing grounding approaches are leading us on a more reliable route for artificial intelligence systems. AI will become even more reliable from continuous developments in retrieval-augmented methods, real-time verification, and quick feedback.

We're not trying to limit AI's abilities; we just want to make them better. With the right setup, AI can be a helpful assistant that not only sounds good but also provides accurate and trustworthy information. The future of AI is about being smart but also being right and responsible.