I. AI Hallucinations: The Core Problem
How big of a problem are AI hallucinations in real-world applications, especially in sensitive fields?
AI hallucinations are a huge problem, the consequences of which can be severe. It’s one thing if AI provides the wrong answer to a homework question, but it’s far more dangerous when it misdiagnoses a health condition or offers poor legal counsel. We are seeing more and more cases of AI hallucinating everything from investment advice to pet concerns, topics that are far too nuanced for a chatbot alone. As people continue to rely on GenAI (79% of ChatGPT users use AI for professional services), accuracy remains a huge concern.
In addition to distrust, there are legal implications. AI companies know their systems are flawed, which is why they hide behind the phrase “consult a professional.” But a disclaimer doesn’t erase the damage. Over 70% of AI responses to health, legal, or veterinary questions include this copout. Yet courts are catching on—Section 230 immunity, which once protected tech companies, no longer applies to AI-generated harm. Liability is here, and lawsuits are coming fast.
Can AI hallucinations be completely eliminated, or are they an inherent limitation?
AI hallucinations are an inevitability – AI alone cannot guarantee quality, accuracy or safety. That’s why it’s important we reconsider the relationship between experts and AI. To ensure accuracy and trust in nascent AI systems, we must have a human in the loop to verify responses and ultimately protect people from misinformation.
Beyond factual errors, what are some of the more subtle or insidious forms AI hallucinations can take?
It’s the nuanced responses far more than the obvious factual errors that pose the greatest risk. AI isn’t just wrong—it’s dangerously reckless because it provides a false sense of confidence. Mental health is a key example. This is an area in which professional nuance is critical and, oftentimes, lifesaving. Professionals are trained for years in how to manage mental health concerns like depression and suicidal ideation. Now, as people use AI for more sensitive queries, they are vulnerable to dangerously misleading responses, especially young people. Not too long ago, a chatbot encouraged a teen to commit suicide. This punctuates clearly the dire need for human involvement in AI.
How does Pearl differentiate between a genuine AI insight and a hallucination?
Pearl leverages a network of 12,000+ seasoned professionals across 700+ professions to validate AI responses with a personalized TrustScore, ranging on a scale from 1-5 to determine how trustworthy the AI answer is.
II. Pearl’s Combined Intelligence Solution
How does Pearl’s “combined intelligence” model specifically reduce the risk of AI hallucinations?
Our human-in-the-loop model is proven to mitigate the risk of AI hallucinations better than other platforms. Pearl conducted internal tests in which users asked questions through the Pearl interface and were given responses from either Pearl, ChatGPT, or Google Gemini, with the underlying LLM being assigned at random. These tests found that:
- Pearl gave answers that were 22% more helpful than ChatGPT or Gemini
- Pearl’s answers were 41% less wrong than ChatGPT and Google Gemini
- What’s the ratio of human input to AI processing in Pearl’s typical workflow?
26% of our customers request to have a human brought into the loop, meaning 26% of the time, they want to escalate a larger conversation with one of the experts in our network.
- How do you ensure consistency and quality from the human experts on your platform?
We only accept the top 5% of expert applicants on our platform. They must pass our stringent screens, including background checks and license verification. We also have internal monitoring in place to ensure experts are delivering high-quality answers to our customers.
- Does Pearl use techniques like RAG or self-reflection to mitigate hallucinations? If so, how? No, we do not use self-reflection to mitigate hallucinations.
III. Pearl’s Approach and Vision for the Future
How does Pearl address the “ultracrepidarian” problem of AI speaking outside its expertise?
Our AI is informed by over 30M proprietary expert answers to question we have been asked in the past 21 years, making our dataset extremely robust. More and higher quality data leads to better AI because it provides more examples of edge cases, rare occurrences, and nuanced patterns, making predictions and classifications more accurate.
What are the key technical challenges in building a system like Pearl?
Building Pearl.com, an AI-powered search tool that seamlessly integrates human expertise, involves several complex technical challenges:
- Data Engineering for RAG – Structuring, optimizing, and scaling data pipelines to enhance AI-driven retrieval, ensuring precision, contextual relevance, and efficient knowledge synthesis.
- Prompt Engineering for Clarifying Questions – Designing AI prompts that accurately detect query complexity, refine ambiguous inputs, and generate the right follow-up questions to enhance user intent understanding.
- Model Selection for Different Tasks – Strategically determining which AI models to use for different functions—such as clarifications, responses, and domain-specific queries (e.g., legal vs. medical)—to optimize both accuracy and contextual fit.
- Real-Time AI-to-Expert Routing – Developing intelligent routing mechanisms that instantly connect AI-generated responses to the most suitable human experts, ensuring seamless validation and high-quality user experiences.
- Quality Assurance for Expert Responses – Expanding scalable QA systems that maintain consistency, reliability, and trust in expert-provided answers across diverse professional domains.
- Managing Latency – Optimizing system architecture to balance AI complexity with response speed, ensuring rapid execution of multi-step AI processes without compromising quality.
How do you see the balance between AI and human expertise evolving in the future?
At Pearl, we believe the future of intelligence is not about choosing between human intuition and AI precision—it’s about uniting them. We seek to forge a world where human intelligence and AI come together seamlessly to build trust, inspire confidence, and drive transformative results.
Our vision doesn’t stop at solving today’s challenges—it seeks to break down barriers that have held society back for decades. Professional expertise, once reserved for the privileged few, can now be accessible to everyone. From medical consulting to legal counsel, Pearl is democratizing access to high-value professional services, powered by AI and anchored in human knowledge.
What advice do you have for others building AI systems where reliability is paramount?
The easy answer isn’t always the right one. Take a look at DeepSeek, the new platform that recently upended the AI space. WSJ found DeepSeek produced dangerous outputs (from a manifesto in defense of Hitler to a social-media campaign to promote self-harm among teens), and it proves that building AI platforms for cheap isn’t always better. Ultimately, human verification is the only way to ensure accuracy and trust.