Why GPT Hallucinates and How KChat Fixes It
Transcript
(00:04) If you have used ChatGPT, you know it can sometimes answer with confidence but still get facts wrong. That’s called a “hallucination.” In this video, we look at why GPT models hallucinate and how a solution like KChat, which uses your own curated knowledge base, can solve the problem. Let’s take a look. The first reason GPT may hallucinate is that its training data is not real time and does not include everything on the internet. As a result, it may lack the context needed to provide the right answer.
(00:49) This is different from Google Search, which constantly indexes the web and serves the latest information. The second and most important reason GPT hallucinates is that it does not know whether the sources it draws from are accurate. GPT is trained to generate plausible text based on context, but it does not verify whether sources are correct. The third reason is also critical: the way you ask questions matters. GPT is generative. The more context you provide and the more specific your question, the better the answer.
(01:32) Our KChat solution solves these problems because it uses Retrieval‑Augmented Generation (RAG). With RAG, the model generates answers grounded in your own database and curated sources. You maintain access to specific knowledge, keep it updated so answers stay current, and ensure factual accuracy because the sources are selected and added by you. You also gain contextual accuracy because RAG systems are typically vertical, focused on well‑defined topics.
(02:18) We will look at two examples of KChat to show how it provides high‑quality answers. In this demo, I show how KChat on our website eliminates hallucinations and addresses the three drivers we discussed. Number one is the knowledge base: in KChat, the knowledge base is curated by you, so answers are always related to your information. Number two is source transparency: we know the sources of the information and KChat links you to them. Number three is question specificity: the more specific your question, the better your answer.
(03:03) As a visitor to Optimal Access, I see a headline like “Custom ChatGPT for your curated content,” built from your curated knowledge base that can include YouTube videos, blog posts, and web pages. Naturally, I ask: How does KChat understand the content of my YouTube videos? Let’s look at the answer. What you see is an answer directly related to your question. KChat lets content creators import their YouTube channel with transcripts and comments for chatbot use. You get the answer plus citations.
(03:50) Example sources include “KChat for YouTube content creators,” “Getting started with KChat,” and “KChat for content creators.” Each source covers the same topic from a slightly different angle. Clicking a source takes you to the corresponding content, where you can watch the full video and learn how to use your YouTube videos with KChat. Because KChat is a RAG system, it minimizes hallucinations for your chatbot. The best way to know is to try it on your own content.
(04:39) Here’s another example with a different type of content. I created a demo site for analysts, content creators, political campaigns, and educators, covering a wide range of topics. The hub has 800+ posts, and I’m consistently impressed by how well KChat finds the right information, which prevents hallucinations. For example, I asked: What is a reasonable percentage of income to spend on rent, and why have rent prices been rising so rapidly across the United States in recent years?
(05:24) The answer is directly on‑topic and in context. Housing is generally considered affordable at roughly 25–30% of income. More importantly, KChat leads the reader to the source of the information. In this case, it links to a talk by economist Dr. Richard Wolff, who explains the background and provides detailed reasoning. A short sound bite won’t educate people, but a primary source will provide the full context. That design—answer plus sources—is core to KChat and a great use of GPT.
(06:12) The best way to evaluate how well this works is to try KChat with your own content. Import articles, blog posts, and YouTube transcripts. Then ask domain‑specific questions and review answers with citations. See for yourself how grounding in curated sources reduces hallucinations and improves trust.
(07:00) Summary: GPT models can hallucinate because they are not real time, they do not verify source quality, and they depend on the specificity of your prompts. KChat’s RAG approach solves these issues by grounding answers in your curated knowledge base, keeping content updated, and linking to original sources for verification and learning.
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