Coze | 3 ways to reduce hallucination for your AI chatbot

The video transcript discusses strategies to reduce hallucinations in AI chatbots, ensuring they provide more accurate responses. The first method involves adding knowledge to the bot, demonstrated through creating a knowledge base for a restaurant or bakery menu using an Excel file. The second strategy is adjusting the model's temperature to achieve a balance between creativity and precision, which is particularly useful for customer service bots. Lastly, the minimum matching degree in the knowledge settings can be lowered to increase the likelihood of the bot referencing its knowledge base. These tips help in creating a more reliable AI chatbot experience for users.

Takeaways

• 📚 Add knowledge to your AI chatbot to reduce hallucinations. Start by creating a knowledge base relevant to the bot's purpose, such as a menu for a restaurant or bakery.
• 🔍 When adding knowledge, you can input data through an interface or upload structured files like Excel or CSV, which helps the bot provide more accurate responses.
• 📈 Ensure data types are correctly set for each field in your knowledge base, such as 'string' for item names, pricing, and URLs.
• 📋 Choose an index, like the item name, to identify each entry in your knowledge base, and provide descriptions for better understanding by the language model.
• 💡 Use the model configuration to select an appropriate model and adjust the temperature setting to balance creativity and precision in the bot's responses.
• ✅ Lower the temperature for more precise and reliable answers, which is especially useful for customer service chatbots.
• 🔧 Adjust the minimum matching degree in the knowledge settings to increase the likelihood that the bot will draw from its knowledge base for answers.
• 📉 By lowering the minimum matching degree, the bot will rely more on its knowledge base and less on random responses.
• 🖼️ If you want your bot to provide image responses, include image URLs in your knowledge base and use specific prompts to output images.
• 🔗 Use semantic search for the bot to find and recommend items based on the context of the conversation.
• 📈 Structured formats like Excel are preferred for adding knowledge because they provide more accurate and reliable data for the bot to use.
• 🛠️ Regularly update and modify the knowledge base to keep the bot's information current and relevant.

Q & A

What is one of the easiest ways to reduce hallucinations in an AI chatbot?

One of the easiest ways to reduce hallucinations in an AI chatbot is by adding knowledge to the bot. This can be done through the interface by creating a new knowledge base with relevant information such as a menu for a restaurant or bakery.

How can you add knowledge to a chatbot using an Excel file?

You can add knowledge to a chatbot using an Excel file by uploading it to the bot's knowledge base. The Excel file should contain structured data such as item names, pricing, images (as URLs), and other relevant information. The bot will then be able to use this data to provide more accurate responses.

What is the purpose of the 'temperature' setting in a chatbot's model configuration?

The 'temperature' setting controls the randomness of the bot's responses. A lower temperature results in more precise and accurate answers, while a higher temperature allows for more creative and random responses. It's adjustable based on the desired level of creativity or precision needed for the bot's tasks.

How can you ensure that a chatbot uses its knowledge base to answer questions?

To ensure a chatbot uses its knowledge base, you can adjust the model's settings. Specifically, you can use the 'automatic call' feature and set the 'minimum matching degree' to a lower number, which increases the likelihood that the bot will refer to the knowledge base for answers.

Why is it important for a chatbot to accurately answer questions based on its knowledge base?

It's important for a chatbot to accurately answer questions based on its knowledge base to provide reliable and consistent information to users. Inaccurate or 'hallucinated' responses can lead to confusion and potential issues, such as the example of an airline losing money due to incorrect policy adherence.

How can you structure the data in the Excel file for the chatbot to understand and use effectively?

The data in the Excel file should be structured in a table format with clear data types for each column. For instance, item names, pricing, and image URLs should be marked as strings, and customer favorites can also be a string. Each column should have a designated index, such as the item name, to serve as the identifier for each item.

What is the role of the 'semantic search' in retrieving information from the knowledge base?

The 'semantic search' uses a vector store or vector database to retrieve relevant queries or recommendations based on the context of the conversation. It helps the chatbot find similarities and provide more accurate responses by searching through the structured knowledge base.

How can you make the chatbot provide image responses from the knowledge base?

You can make the chatbot provide image responses by including image URLs in the Excel file and ensuring there's a prompt in the bot's settings that instructs it to output images in a specific format. This allows the bot to retrieve and display images as part of its responses.

What are the three tips mentioned to make a chatbot more reliable and reduce hallucinations?

The three tips are: 1) Use knowledge, preferably from a structured source like an Excel file. 2) Change the model's temperature to lower it for more precise answers. 3) Adjust the knowledge settings to lower the minimum matching degree, which increases the chance that the model will draw from its knowledge to answer questions.

Why is it beneficial to use an Excel file for a chatbot's knowledge base instead of a document or URL?

An Excel file is beneficial because it provides a structured format that is easily parsed and understood by the chatbot. This structure allows for more accurate and reliable information retrieval, which in turn leads to better responses and fewer hallucinations.

How can adjusting the model's temperature help in controlling the chatbot's creativity?

Adjusting the model's temperature allows you to control the balance between creativity and precision in the chatbot's responses. A higher temperature setting will make the bot more creative, which is suitable for tasks like writing fantasy stories. Conversely, a lower temperature setting will make the bot more precise, which is ideal for customer service interactions.

What is the significance of setting a proper 'minimum matching degree' when using a knowledge base?

The 'minimum matching degree' determines how closely the bot's queries must match the knowledge base's content before it draws from that knowledge. By setting a lower degree, the bot is more likely to refer to the knowledge base for answers, ensuring that the responses are relevant and reducing the chance of irrelevant or 'hallucinated' responses.

Outlines

00:00

📚 Adding Knowledge to a Bot for Enhanced Accuracy

The first paragraph explains how to reduce hallucinations in a bot by adding knowledge through an interface. It describes creating a new knowledge base, specifically for a restaurant or bakery, with the menu as the knowledge base. It details the process of adding units to the knowledge base, including uploading an Excel file with item names, pricing, images, and customer favorites. The paragraph also covers how to manage and modify the CSV file and ensure data types are correctly assigned. It concludes with how to integrate the knowledge base into the bot workspace and use it to answer questions accurately.

05:03

🔍 Enhancing Bot Reliability with Knowledge and Model Settings

The second paragraph focuses on improving a bot's reliability and reducing hallucinations by using knowledge and adjusting model configurations. It discusses the importance of using a structured Excel format for knowledge input and the role of model temperature in controlling the bot's creativity versus precision. The paragraph also explains how to use automatic call and semantic search to ensure the bot draws from the knowledge base when answering questions. It highlights the significance of adjusting the minimum matching degree to increase the likelihood of the bot using knowledge to provide answers. The paragraph concludes with tips for making a chatbot more reliable and reducing hallucinations, including using knowledge, adjusting model temperature, and modifying the minimum matching degree.

Keywords

Hallucination

In the context of AI chatbots, 'hallucination' refers to the generation of responses that are not grounded in the provided knowledge base or context. It's a deviation from the expected or accurate information. The video discusses ways to reduce this phenomenon to improve the reliability of AI chatbots.

Knowledge Base

A 'knowledge base' is a structured collection of data that an AI chatbot can use to provide responses. In the video, it's used to store information about a bakery's menu, which the chatbot can then utilize to answer questions accurately.

Excel File

An 'Excel file' is a type of spreadsheet document created using Microsoft Excel. It's used in the video to format and upload data into the knowledge base, such as item names, pricing, and images, which the chatbot can then reference.

CSV File

A 'CSV file' stands for Comma Separated Values and is a type of file used to store tabular data, typically in plain text. The video mentions it as an alternative format for uploading structured data into the chatbot's knowledge base.

Model Configuration

Refers to the settings and parameters that define how an AI chatbot's language model operates. In the video, adjusting the model configuration, particularly the 'temperature', is suggested as a method to control the chatbot's responses and reduce hallucinations.

Temperature

In AI language models, 'temperature' is a hyperparameter that controls the randomness of the model's responses. Lowering the temperature results in more predictable and less random answers, which is demonstrated in the video as a way to make the chatbot's responses more precise.

Semantic Search

A 'semantic search' is a method of searching that focuses on the meaning and intent behind the search terms rather than just the words themselves. The video discusses using semantic search to help the chatbot find relevant information within the knowledge base.

Minimum Matching Degree

The 'minimum matching degree' is a setting that determines how closely a search query must match the content in the knowledge base for the bot to use that content in its response. Lowering this degree, as shown in the video, increases the likelihood that the bot will draw from the knowledge base.

Vector Store/Database

A 'vector store' or 'vector database' is a type of database that stores and retrieves data based on vectors, which are mathematical representations of items in a multidimensional space. The video explains that the knowledge base uses a vector store to retrieve relevant information.

Data Types

In the context of the video, 'data types' refer to the classification of data into its type, such as string, number, or boolean. Ensuring correct data types in the knowledge base, as shown in the video, is crucial for the chatbot to accurately interpret and use the data.

Customer Favorite

This term in the video is used to denote items that are popular among customers, as indicated in the Excel file's data. The chatbot can use this information to make recommendations, which is an example of using the knowledge base to enhance user experience.

Highlights

Adding knowledge to your AI chatbot can reduce hallucinations by creating a knowledge base specific to the bot's purpose.

Knowledge bases can be created through an interface by adding units and using structured data formats like Excel or CSV files.

Excel files can be uploaded and managed within the knowledge base, allowing for easy updates and modifications.

Data types for each field in the knowledge base should be checked to ensure accuracy and consistency.

Choosing an index for the knowledge base helps the bot identify and retrieve relevant information more effectively.

Adjusting the model's temperature can control the randomness of the bot's responses, making it more or less creative as needed.

Lowering the temperature setting can lead to more precise and accurate answers from the bot.

Using the bot's knowledge base can prevent it from providing inaccurate or irrelevant information.

Semantic search in the knowledge settings can help the bot retrieve relevant information based on the context of the conversation.

Adjusting the minimum matching degree can increase the likelihood of the bot using the knowledge base to answer questions.

The bot can retrieve and display images from the knowledge base if the image URLs are included in the data.

Using specific prompts and including image URLs in the knowledge base can enable the bot to provide image-based responses.

To improve reliability and reduce hallucinations, use structured knowledge, adjust the model's temperature, and modify the minimum matching degree.

Using an Excel sheet for knowledge is more precise due to its structured format compared to documents or URLs.

The bot's knowledge base can be continuously updated and configured to reflect changes in information or requirements.

Properly configuring the bot can prevent business-critical issues such as incorrect policy adherence leading to financial losses.

AI chatbots can provide a better user experience when configured to leverage knowledge bases and adjusted settings.