In-depth guide to building a custom GPT-4 chatbot on your data
GPT-4, the latest language model by OpenAI, brings exciting advancements to chatbot technology. These intelligent agents are incredibly helpful in business, improving customer interactions, automating tasks, and boosting efficiency. They can also be used to automate customer service tasks, such as providing product information, answering FAQs, and helping customers with account setup. This can lead to increased customer satisfaction and loyalty, as well as improved sales and profits.
Chatbots powered by GPT-4 can scale across sales, marketing, customer service, and onboarding. They understand user queries, adapt to context, and deliver personalized experiences. By leveraging the GPT-4 language model, businesses can build a powerful chatbot that can offer personalized experiences and help drive their customer relationships.
In this article, we'll show you how to build a personalized GPT-4 chatbot trained on your dataset.
What is GPT-4?
GPT-4 is a large multimodal transformer model developed by OpenAI that accepts image and text inputs and emits text outputs. It shows human-level performance on various professional and academic benchmarks.
GPT-4 has shown amazing reasoning capabilities. It has passed many difficult exams like SAT and even the bar exam. These capabilities make it the best model out there. We can use GPT4 to build sales chatbots, marketing chatbots and do a ton of other business operations.
As GPT is a General Purpose Technology it can be used in a wide variety of tasks outside of just chatbots. It can be used to generate ad copy, and landing pages, handle sales negotiations, summarize sales calls, and a lot more. In this article, we will focus specifically on how to build a GPT-4 chatbot on a custom knowledge base.
How is GPT-4 better than other GPT models?
GPT-4 promises a huge performance leap over GPT-3 and other GPT models, including an improvement in the generation of text that mimics human behavior and speed patterns. GPT-4 is able to handle language translation, text summarization, and other tasks in a more versatile and adaptable manner. GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than its predecessors GPT-3 and ChatGPT.
GPT-4’s enhanced capabilities can be leveraged for a wide range of business applications. Its improved performance in generating human-like text can be used for tasks such as content generation, customer support, and language translation. Its ability to handle tasks in a more versatile and adaptable manner can also be beneficial for businesses looking to automate processes and improve efficiency. GPT-4 is able to follow much more complex instructions compared to GPT-3 successfully.
Here is a performance comparison between GPT-3 and GPT-4, provided in their technical report:
Why build a custom GPT-4 Chatbot?
Large Language Models or LLMs are trained on a massive dataset of text and code. They perform well for most of the general scenarios and for some very domain-specific settings too like summarizing sales calls. But for business use cases, they need to be fine-tuned to the user’s needs. This is usually done by modifying the prompt passed to the model, this process is called prompt engineering. Here are some reasons why to customize base GPT-4 or GPT-3 models:
GPT-4 Knowledge Base is Limited
Even though trained on massive datasets, LLMs always lack some knowledge about very specific data. Data that is not publically available is the best example of this. Data like private user information, medical documents, and confidential information are not included in the training datasets, and rightfully so. This means if you want to ask GPT questions based on your customer data, it will simply fail, as it does not know of that. Or it might hallucinate, that is to give wrong answers or replies.
Another huge way GPT-4 is limited is that it lacks knowledge of events that occurred after September 2021. The training dataset is limited and is not updated, and neither does the model learns from user interactions. This is a huge limitation on the model’s capabilities.
In the article, we will cover how to use your own knowledge base with GPT-4 using embeddings and prompt engineering.
To reduce hallucinations
As mentioned, GPT models can hallucinate and provide wrong answers to users’ questions. This happens because models are a class of autoregressive models. Meaning, at the core they work by predicting the next word in the conversation. This means if the model is not prompted correctly, the outputs can be very wrong.
To reduce this issue, it is important to provide the model with the right prompts. This means providing the model with the right context and data to work with. This will help the model to better understand the context and provide more accurate answers. It is also important to monitor the model’s performance and adjust the prompts accordingly. This will help to ensure that the model is providing the right answers and reduce the chances of hallucinations.
To control conversation and tonality
Sometimes it is necessary to control how the model responds and what kind of language it uses. For example, if a company wants to have a more formal conversation with its customers, it is important that we prompt the model that way. Or if you are building an e-learning platform, you want your chatbot to be helpful and have a softer tone, you want it to interact with the students in a specific way.
It is also important to limit the chatbot model to specific topics, users might want to chat about many topics, but that is not good from a business perspective. If you are building a tutor chatbot, you want the conversation to be limited to the lesson plan. This can usually be prevented using prompting techniques, but there are techniques such as prompt injection which can be used to trick the model into talking about topics it is not supposed to.
To personalize GPT to your needs
A personalized GPT model is a great tool to have in order to make sure that your conversations are tailored to your needs. GPT4 can be personalized to specific information that is unique to your business or industry. This allows the model to understand the context of the conversation better and can help to reduce the chances of wrong answers or hallucinations. One can personalize GPT by providing documents or data that are specific to the domain. This is important when you want to make sure that the conversation is helpful and appropriate and related to a specific topic. Personalizing GPT can also help to ensure that the conversation is more accurate and relevant to the user.
The personalization feature is now common among most of the products that use GPT4. Users are allowed to create a persona for their GPT model and provide it with data that is specific to their domain. This helps to make sure that the conversation is tailored to the user’s needs and that the model is able to understand the context better. For example, if you are a copywriter, you can provide the model with examples of your work and prompt it with various copywriting techniques to help it understand the context and generate better copy. Custom chatbots are the future.
How to build a custom GPT-4 chatbot?
A custom chatbot at the core is a combination of two core things, prompts and providing the right context. Prompts control the model behavior and provide the model with a guideline on how to interact with the user. Context on the other hand is the knowledge base using which the model answers the user queries. Both of these components are necessary for a good chatbot and should be tailored to the specific use case. Here is the pipeline we use to build custom chatbots:
Let’s break down the concepts and components required to build a custom chatbot.
Chatbot
The chatbot is a large language model fine-tuned for chatting behavior. ChatGPT/GPT3.5, GPT-4, and LLaMa are some examples of LLMs fine-tuned for chat-based interactions. It is not necessary to use a chat fine-tuned model, but it will perform much better than using an LLM that is not. We will use GPT-4 in this article, as it is easily accessible via GPT-4 API provided by OpenAI.
Chatbot here is interacting with users and providing them with relevant answers to their queries in a conversational way. It is also capable of understanding the provided context and replying accordingly. This helps the chatbot to provide more accurate answers and reduce the chances of hallucinations. Based on user interactions, the chatbot’s knowledge base can be updated with time. This helps the chatbot to provide more accurate answers over time and personalize itself to the user's needs.
Embedding Generator
Our chatbot model needs access to proper context to answer the user questions. This is basically how you make GPT-4 personalized to your data. Embeddings are at the core of the context retrieval system for our chatbot. We convert our custom knowledge base into embeddings so that the chatbot can find the relevant information and use it in the conversation with the user.
Embeddings allow us to map our data into a vector space, which can be used by the model to understand the context. Embeddings are learned by the LLMs during training. The core property of embeddings is that they carry the semantic meaning of the sentences in the form of vectors. This means embedding vectors of two sentences that are similar in nature such as: “I live in New York.” and “I live in Los Angeles.” will have their embedding vectors very similar. Whereas a sentence like “James has a dog.” will have its embedding very different from the two. This property of embeddings allows us to retrieve relevant documents to answer the user query.
We will use a custom embedding generator to generate embeddings for our data. One can use OpenAI embeddings or SBERT models for this generating embeddings. Also, this process can be decoupled from the rest of the pipeline.
Embedding Large Documents
If you have a large number of documents or if your documents are too large to be passed in the context window of the model, we will have to pass them through a chunking pipeline. This will make smaller chunks of text which can then be passed to the model. This is usually done even if the documents are small. This process ensures that the model only receives the necessary information, too much information about topics not related to the query can confuse the model.
Here’s a quick overview of our chunking pipeline:
Retrieving Documents
Once we have our embeddings ready, we need to store and retrieve them properly to find the correct document or chunk of text which can help answer the user queries. As explained before, embeddings have the natural property of carrying semantic information. If the embeddings of two sentences are closer, they have similar meanings, if not, they have different meanings. We use this property of embeddings to retrieve the documents from the database. The query embedding is matched to each document embedding in the database, and the similarity is calculated between them. Based on the threshold of similarity, the interface returns the chunks of text with the most relevant document embedding which helps to answer the user queries.
Here is a good representation by OpenAI of how embeddings work:
To store embeddings, we use special databases called Vector Databases. These databases, store vectors in a way that makes them easily searchable. Some good examples of these kinds of databases are Pinecone, Weaviate, and Milvus.
Once we have the relevant embeddings, we retrieve the chunks of text which correspond to those embeddings. The chunks are then given to the chatbot model as the context using which it can answer the user’s queries and carry the conversation forward.
For example, if you were building a custom chatbot for books, we will convert the book’s paragraphs into chunks and convert them into embeddings. Once we have that, we can fetch the relevant paragraphs required to answer the question asked by the user.
Prompt Tuning for Language and Tonality
It is very important that the chatbot talks to the users in a specific tone and follow a specific language pattern. This is why prompt tuning is necessary. We want the chatbot to have a personality based on the task at hand. If it is a sales chatbot we want the bot to reply in a friendly and persuasive tone. If it is a customer service chatbot, we want the bot to be more formal and helpful. We also want the chat topics to be somewhat restricted, if the chatbot is supposed to talk about issues faced by customers, we want to stop the model from talking about any other topic.
To control the language and the topics of the chatbot, we modify the prompts accordingly. Here are some techniques we use:
Few-Shot Prompting
The model can be provided with some examples of how the conversation should be continued in specific scenarios, it will learn and use similar mannerisms when those scenarios happen. This is one of the best ways to tune the model to your needs, the more examples you provide, the better the model responses will be.
Here is an example of few shot prompting:
Here we provided GPT-4 with scenarios and it was able to use it in the conversation right out of the box! The process of providing good few-shot examples can itself be automated if there are way too many examples to be provided. We can use an embedding-based pipeline for this too.
Parameter Tuning
Another very important thing to do is to tune the parameters of the chatbot model itself. This part is often overlooked. All LLMs have some parameters that can be passed to control the behavior and outputs.
- Temperature: This parameter is used to control the randomness of the outputs. Higher temperature values (e.g., 1.0) result in more varied and creative outputs, while lower values (e.g., 0.5) lead to more focused and deterministic responses. It is important to test the outputs of the model with a smaller temperature and then increase it slowly to find the value which works the best for you.
- Top-P: Top-P or nucleus sampling helps in selecting how many of the tokens should be considered when predicting the next word or token. It sets a threshold value (e.g., 0.8) to consider the most probable words, balancing diversity and coherence in the generated text.
- Maximum Length: This parameter sets the maximum allowed tokens or characters in the generated text to control its length and ensure relevance. This also depends on the use case at hand. If we are building a simple chatbot, it makes sense to use a smaller maximum length. If the chatbot is supposed to “explain” things to the user, then we are going to need a bigger maximum length value.
- Frequency and Presence Penalties: Both of these parameters reduce repetitive words or phrases in the generated text by assigning a penalty, promoting diversity. The frequency penalty does this based on the number of times the word has already appeared in the output text. The presence penalty only takes into account the presence of the word, the frequency of the word does not matter, it is a much harder penalty.
Building a Custom Chatbot with Langchain
With new Python libraries like LangChain, AI developers can easily integrate Large Language Models (LLMs) like GPT-4 with external data. LangChain works by breaking down large sources of data into "chunks" and embedding them into a Vector Store. This Vector Store can then be queried by the LLM to generate answers based on the prompt. It basically automates the chatbot pipeline completely.
Langchain provides developers with components like index, model, and chain which make building custom chatbots very easy. You can read more about the core components of langchain here.
Traditional NLP Chatbots vs GPT-4
Before GPT based chatbots, more traditional techniques like sentiment analysis, keyword matching, etc were used to build chatbots. These chatbots used rule-based systems to understand the user’s query and then reply accordingly. This approach was very limited as it could only understand the queries which were predefined.
The classifier can be a machine learning algo like Decision Tree or a BERT based model that extracts the intent of the message and then replies from a predefined set of examples based on the intent. This approach is very limited and non-flexible. GPT models can understand user query and answer it even a solid example is not given in examples.
Here are some ways how GPT-4 and other GPT based models solve limitations of the traditional chatbots:
GPT models are more flexible
As mentioned above, traditional chatbots follow a rule based approach. They are not flexible and require a lot of human oversight. Businesses have to spend a lot of time and money to develop and maintain the rules. Also, the rules are often rigid and do not allow for any customization. This is not a very scalable model.
On the other hand, GPT-4 offer a more flexible approach. These models use large transformer based networks to learn the context of the user’s query and generate appropriate responses. This allows for much more personalized replies as it can understand the context of the user’s query. It also allows for more scalability as businesses do not have to maintain the rules and can focus on other aspects of their business. These models are much more flexible and can adapt to a wide range of conversation topics and handle unexpected inputs.
GPT models have a better understanding of user query
Models like GPT-4 have been trained on large datasets and are able to capture the nuances and context of the conversation, leading to more accurate and relevant responses. GPT-4 is able to comprehend the meaning behind user queries, allowing for more sophisticated and intelligent interactions with users. This improved understanding of user queries helps the model to better answer the user’s questions, providing a more natural conversation experience.
Traditional techniques like intent-classification bots fail terribly at this because they are trained to classify what th user is saying into predefined buckets. Often it is the case that user has multiple intents within the same the message, or have a much complicated message than the model can handle. GPT-4 on the other hand “understands” what the user is trying to say, not just classify it, and proceeds accordingly.
GPT models can be customized for any context
GPT-4 can be customized very quickly with some prompt engineering. If you are trying to build a customer support chatbot, you can provide some customer service related prompts to the model and it will quickly learn the language and tonality used in customer service. It will also learn the context of the customer service domain and be able to provide more personalized and tailored responses to customer queries. And because the context is passed to the prompt, it is super easy to change the use-case or scenario for a bot by changing what contexts we provide.
Traditional chatbots on the other hand might require full on training for this. They need to be trained on a specific dataset for every use case and the context of the conversation has to be trained with that. This is very limiting. With GPT models the context is passed in the prompt, so the custom knowledge base can grow or shrink over time without any modifications to the model itself.
Why use Custom Chatbots for your Business?
Custom chatbots provide a lot of benefits for businesses. They provide a more personalized and efficient customer experience by offering instant responses to user queries and automating common tasks. Custom chatbots can handle a large volume of inquiries simultaneously, reducing the need for human teams and increasing operational efficiency. Additionally, they can be integrated with existing systems and databases, allowing for seamless access to information and enabling smooth interactions with customers. Businesses can save a lot of time, reduce costs, and enhance customer satisfaction using custom chatbots.
Want to build a Custom Personalized Chatbot?
If you are looking to build chatbots trained on custom datasets and knowledge bases, Mercity.ai can help. We specialize in developing highly tailored chatbot solutions for various industries and business domains, leveraging your specific data and industry knowledge. Whether you need a chatbot optimized for sales, customer service, or on-page ecommerce, our expertise ensures that the chatbot delivers accurate and relevant responses. Contact us today and let us create a custom chatbot solution that revolutionizes your business.