How to Make AI Tell the Truth

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.

Why AI Hallucinations Happen

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.

Training a Model vs. Leveraging Existing LLMs

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:

• Data Flexibility: The content store can be updated and new data can be added at any time without retraining the model, enabling instant, cost-effective changes.
• Accuracy: By accessing a structured database, RAG and PGA ensure that responses are precise and relevant, as the AI retrieves information from curated, high-quality sources.

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.

Building the Right Architecture: RAG or PGA

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.

RAG: Reliable Answers for Complex Questions

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:

1. Build a Content Library: RAG divides content into small, searchable chunks (such as sentences or paragraphs) and stores each as a vector.
2. Match the Query: The user’s question is converted into a vector, and a pgvector proximity search finds the most relevant content chunks.
3. Provide Context: The AI is prompted with these relevant pieces of information to generate a precise, context-aware response.

PGA: Consistent, Expert-Approved Responses

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:

1. Create a Q&A Database: Expert-vetted questions and answers are stored as vectors.
2. Match the Query: PGA converts the user’s question into a vector and finds the closest match in the Q&A database using vector proximity.
3. Deliver a Consistent Response: The AI is provided with the verified answer and rephrases as needed to match the context of the interaction with the user. Users receive the same reliable answer every time, which is critical in regulated fields.

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.

Future-Proofing AI for Scalability

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.

What To Watch For:

• Avoid Vendor Lock-In: Today’s top AI provider might not meet your needs in the future. Opt for flexible architectures that easily integrate new technologies.
• Scale Smartly: When developing a new app, using third-party AI services is a smart way to accelerate development and gather market feedback quickly. However, as the app scales, running LLM models on your own infrastructure can be more cost-effective. It is best to build the app with this transition in mind from the start.

This strategy ensures that your AI infrastructure can grow with your business, maintaining both flexibility and control.

Maintaining AI Reliability with High-Quality Data

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.

What You Can Do:

• Use Domain-Specific Data: Ensure your AI system is equipped with high-quality, industry-specific data.
• Invest in Continuous Data Updates: Outdated data leads to outdated answers. Make sure your AI has access to up-to-date information to keep its responses accurate and relevant.
• Audit Regularly: Test your AI’s accuracy regularly to ensure it meets your business needs.
• Implement Guardrails: Teach your AI to say “I don’t know” instead of making up answers, reducing the risk of harmful misinformation.

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.

The Path to Truthful AI

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:

• Leverage RAG and PGA to improve domain-specific accuracy and reduce errors.
• Build a scalable, robust architecture using tools like Postgres with pgvector to enable fast, accurate data retrieval.
• Ensure future flexibility by avoiding vendor lock-in, allowing your AI to evolve with your business needs.
• Prioritize high-quality, real-time data and regularly audit your AI for accuracy.

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.