In today’s AI-driven landscape, misinformation isn’t just a glitch — it’s a major pain point for tech leaders that can lead to lost trust, damaged reputations, and wasted time. From chatbots providing false information to decision-making models making errors, AI hallucinations are a real concern. So, how can you ensure your AI tells the truth?
At Six Feet Up, we’ve helped clients tackle this challenge head-on. Here’s how you can apply proven strategies to minimize AI inaccuracies and hallucinations.
Large language models (LLMs) like ChatGPT and Gemini are powerful tools for handling a wide range of topics. However, their broad training often means they act as generalists rather than experts. When faced with unclear or incomplete data, these models tend to “guess” instead of admitting “I don’t know.” This tendency arises because the model’s primary goal is to provide an answer, even if it’s not entirely accurate.
One of our clients experienced this firsthand: their AI system was producing slow, unreliable, and often inaccurate responses. Upon investigation, we discovered that incomplete training data and a lack of focused oversight were driving these AI “hallucinations.”
Understanding the root causes of hallucinations is the first step toward preventing them. With the right techniques, tech leaders can mitigate these risks and increase their AI’s reliability and accuracy.
When it comes to improving AI accuracy, tech leaders have two primary choices: train a custom model on a specific dataset or leverage existing LLMs, like OpenAI’s models, with techniques like Retrieval-Augmented Generation (RAG) or Pre-Generated Answers (PGA).
Training a custom model from scratch requires substantial computing resources (think high-end GPUs or cloud infrastructure), which translates to high costs and lengthy processing times. Additionally, any update to the information requires retraining, a costly and time-consuming process.
RAG and PGA offer a more flexible approach. By tapping into existing, pre-trained models and providing them with data from an external knowledge base or Q&A database (content store), tech leaders can enjoy key advantages including:
Postgres with pgvector powers both RAG and PGA by enabling fast, precise data retrieval. With pgvector, content is stored as vectors, allowing AI to quickly and reliably access relevant answers without retraining.
By leveraging existing LLMs with RAG and PGA, tech leaders can develop accurate, adaptable AI solutions while avoiding the heavy costs and delays associated with model retraining.
RAG and PGA offer targeted solutions to meet different business needs. These techniques use trusted, verified content to drastically reduce the risk of misinformation or fabrication.
Imagine a customer asking a specific, technical question about a product. An incorrect response could damage trust and create frustration. RAG solves this by providing the AI with data from a reliable knowledge base (like internal documentation or past support tickets), allowing it to respond with contextually accurate information.
How RAG Works:
In regulated industries like healthcare or finance, consistent answers are essential. Imagine a patient asking an AI-powered healthcare platform about common symptoms or a professional seeking reliable tax advice. PGA ensures that every response is expert-approved and consistent by drawing from a library of pre-verified Q&A pairs.
How PGA Works:
By building AI systems with pgvector and these retrieval techniques, tech leaders can create scalable, accurate AI solutions that build trust, reduce errors, and support growth. Investing in this architecture delivers consistent, reliable answers — essential for staying competitive in an AI-driven world.
As your business grows, your AI needs will evolve too. That’s why it’s essential to build systems that are both flexible and scalable. At Six Feet Up, we focus on creating vendor-independent software, giving tech leaders the freedom to adapt. By using tools like LiteLLM, you can easily interact with a variety of AI models — from third-party services like OpenAI to locally hosted platforms like Ollama.
This approach lets businesses get up and running quickly with third-party AI services. Over time, as your needs change, you can switch to self-hosted models for better control, lower costs, and more reliable scaling.
This strategy ensures that your AI infrastructure can grow with your business, maintaining both flexibility and control.
AI models are only as accurate as the data they’re built on—a universal truth that also applies to models using RAG and PGA techniques. Simply adding more data isn’t enough; the data must be accurate, relevant, and consistently updated.
By following these steps, tech leaders can ensure their AI systems remain reliable and accurate as their businesses scale, helping you avoid costly errors caused by outdated or poor-quality data.
Misinformation and AI hallucinations are challenges that can be solved with the right strategies. To ensure your AI systems deliver reliable, accurate results, tech leaders should:
By adopting these approaches, tech leaders can build AI systems that enhance customer trust, reduce risks, and drive business success in an AI-powered world.
Now is the time to evaluate your AI systems, address gaps in data quality or reliability, and explore how RAG and PGA can elevate your AI's performance. Contact Six Feet Up to discuss how we can help make your AI systems accurate, trustworthy, and scalable.
Six Feet Up, Inc.
11208 Windermere Blvd.
Fishers, IN 46037 - USA
Phone: +1 (317) 861-5948
Email: info@sixfeetup.com
