How to Prevent AI Hallucinations and Ensure Accurate Chatbot Responses
Artificial intelligence (AI) technology has become increasingly sophisticated, with chatbots being used in various applications. However, there is an ongoing concern about AI hallucinations, where chatbots provide inaccurate or nonsensical responses. To address this issue, recent research has shed light on how to prevent AI hallucinations and ensure accurate chatbot responses.
In a previous article, Tage wrote about ways to prevent ChatGPT, a popular chatbot model, from hallucinating. Now, let’s delve deeper into one specific method that completely avoids AI hallucinations. Before we explore this method, let’s first understand the process of creating a custom ChatGPT chatbot.
To create a chatbot, we employ prompt engineering using an SQL database with VSS (Vector Space Search) capabilities. Some might argue that this is akin to jailbreaking ChatGPT. However, instead of allowing ChatGPT to go haywire, we significantly restrict its capabilities, enabling it to only answer questions that are related to the data present in our SQL database. Creating your custom chatbot can help you better understand the process.
The initial step in creating a chatbot involves crawling and scraping your website. This crawling process dissects your website into training snippets. Each image found on your pages and each section, typically consisting of an Hx element with its accompanying paragraphs, becomes a training snippet. These snippets are then inserted into an SQL database. Our scraper provides more information about this process.
Next, the training snippets created during the crawling process are vectorized using OpenAI’s embeddings API. This vectorization entails creating a 1,520-dimensional vector that describes the trajectory of each training snippet. These vectors are then used for AI searches through your VSS/SQL database later.
When a user poses a question, we create a similar vector for that question. This enables us to calculate the distance between the question and each training snippet in the SQL database. This distance measurement helps us match the question to relevant training snippets. Once these matching snippets are identified, they are concatenated into a single string, forming the context necessary for ChatGPT to answer the specified question.
It’s important to note that we don’t ask ChatGPT to answer questions; rather, we already have the answer. We instruct ChatGPT to transcribe the answer into coherent sentences and phrases based on the user’s question. This method ensures accurate responses tailored to the given context.
To avoid AI hallucinations, one simple instruction can be added to ChatGPT. For example, a prompt like, If you cannot find the answer to my question in the specified context, respond with ‘I am sorry, but I don’t know the answer. Could you please provide some keywords?’ By including this instruction, ChatGPT will only answer when the context created from the training data contains the answer. This prevents ChatGPT from providing inaccurate or nonsensical responses.
By implementing this approach, we effectively dumb down ChatGPT and teach it additional information, ensuring it knows only what we want it to know. This focused knowledge eliminates AI hallucinations, making ChatGPT highly reliable within the defined scope.
In conclusion, preventing AI hallucinations and guaranteeing accurate chatbot responses is indeed possible. By carefully constructing the context for chatbot training, adding specific instructions, and leveraging vectorized representations, we can ensure that chatbots provide precise and relevant information to users. The process of customizing chatbots allows them to specialize in specific subject areas while avoiding the risk of unreliable responses. With these techniques in place, the era of AI hallucinations can be effectively mitigated, making AI-powered chatbots more dependable than ever before.
