What Is Custom GPT? Everything You Need Know About It

Did you catch what happened last week? OpenAI’s boss, Sam Altman, kind of teased Elon Musk while talking up GPT Builder’s cool features. Altman’s points make sense, especially because they’ve made ChatGPT way better with some recent updates.

Now, it’s like a total game-changer, showing us how powerful customized AI can be. And that is none other than Custom GPT! But what is custom GPT? You may wonder.

After all the talk and a bit of confusion, ChatGPT now has this cool custom GPT feature. It’s a big deal because it lets anyone make their own GPT applications without needing to be a coding expert.

Sounds awesome! Isn’t it? 

So, without wasting a single second let’s hop into our exploration on- What is Custom GPT? Everything you need to know about it!

(A) What is Custom GPT?

First of all, let’s go through the definition.

Custom GPT refers to the ability to create your own custom version of the GPT (Generative Pre-trained Transformer) language model, tailoring it to specific needs or tasks.

Didn’t get it? Let me break it down for you in easy terms.

 Alright, so Custom GPT means you can make your own special version of the GPT language model. Think of it like having a personal assistant that really understands you. 

OpenAI set up a system where you and even businesses can create your own AI helper using the ChatGPT model. This special helper called a custom GPT, can be trained on your own stuff.

So, if you teach it about things important to you, it becomes super good at giving accurate and trustworthy responses based on that specific info. It’s like having an AI buddy that’s all about you!

(B) Reasons to use Custom GPT: What’s the need to create a personalized GPT-4 Chatbot?

Now, you must be wondering if people are doing fine with GPT-4. Then what’s the need to customize it? Well, if you think so, I must say that you aren’t aware of the lack of GPT-4 chatbots.

Below are some reasons for building a custom GPT-4 Chatbot-

Let’s dive into the details-

(B.1) Limited Knowledge Base of GPT-4

Ever noticed that chatbots like GPT-4 might not know specific stuff? That’s because they miss out on private or non-public info like medical records. Also, the training data they learned from stops in September 2021. 

To fix this, you can add your own knowledge using fancy things like embeddings and prompt engineering. It’s like teaching your chatbot new tricks!

(B.2) Avoiding Hallucinations

Imagine if your chatbot starts saying weird things. That’s what happens when it hallucinates, predicting the next word wrong. To stop this, you need to ask it questions the right way, give it the proper info, and keep an eye on how it’s doing. 

Adjustments to your prompts are key to keeping them on track and making sure they give you accurate info.

(B.3) Steering Conversations and Tone

You wouldn’t want your chatbot to be too casual with customers or too serious when it’s helping students, right? Customizing helps control how your chatbot talks. It’s like being the director of your chatbot’s script. You can also make sure it only talks about what matters for your business, avoiding off-topic chats. Tricks like prompt injection help keep it focused.

(B.4) Personalizing for Your Needs

Now, think about making your chatbot super personalized. You can tailor it to know everything specific to your business or industry. This means less chance of it messing up or saying things that don’t make sense. You do this by giving it documents or data that matter to your world. Many products are doing this now, letting you create a special version of your chatbot that really understands you. 

It’s like having your own chatbot butler! And trust me, this kind of personalization is the future of chatbots.

(C) How to build a custom GPT-4 chatbot?

Crafting your personalized chatbot boils down to two main things: prompts and context. With prompts, you steer how the model behaves, providing a roadmap for interacting with you. On the flip side, context acts as the knowledge base that empowers the model to tackle your queries. 

For a top-notch chatbot experience, it’s crucial to customize both these components to match your specific needs. 

To build a custom GPT-4 chatbot, you need to go through the following steps described in the table- 

Let me guide you through the process of building a chatbot tailored just for you-

Step 1: Learn the Chatbot Basics

Chatbot is basically a big language smarty, fine-tuned just for chatting. There are different ones like ChatGPT/GPT3.5, GPT-4, and LLaMa, all tuned for chat-style talks.

So, your chatbot is out here, chatting it up with users, giving them spot-on answers in a friendly chat vibe. It’s pretty smart too, understands the context you throw at it, and shoots back responses that make sense. That’s the trick to getting accurate answers and steering clear of any weird hallucinations. 

Plus, the more you and others chat with it, the smarter it gets. It keeps updating its knowledge base, making sure it gives you even better and more personalized answers as time goes on.

Step 2: Embedding Generator

To ensure the chatbot model gives accurate answers, it needs to tap into the right context. This is essentially how you personalize GPT-4 with your data. The heart of your chatbot’s context retrieval system lies in embeddings. 

You can transform your custom knowledge base into embeddings, enabling the chatbot to locate pertinent information and incorporate it into conversations with users.

Embeddings play a key role in mapping your data into a vector space, providing the model with a way to grasp the context. During training, Language Models (LLMs) learn these embeddings. The crucial characteristic of embeddings is their ability to encapsulate the semantic meaning of sentences in vector form. 

Essentially, embedding vectors for similar sentences, like “I live in Kolkata.” and “I live in New Delhi.”, will be very alike. On the other hand, a sentence like “Daizy has a parrot” will have a significantly different embedding. This property of embeddings will allow to retrieval of relevant documents for answering user queries.

Step 3: Embedding Large Documents

If you’ve got heaps of documents or they’re too big to fit into the model’s context window, here’s the trick: run them through a chunking pipeline. This breaks them into smaller bits that can then be fed to the model. We often do this even for small documents. Why? Well, it ensures the model only gets what it needs. 

Tossing in too much info about stuff not related to your question can throw the model off. So, chunking it up keeps things clear and helps your chatbot stay on point with the right info.

Step 4: Retrieving Documents

Now that you’ve got your embeddings ready, let’s handle storing and fetching them the right way to pinpoint the exact document or text chunk that can tackle your queries. Remember, embeddings naturally carry semantic info. If the embeddings of two sentences are close, they mean similar things; otherwise, they’re different. 

The Custom GPT uses this cool property to fish out documents from the database. Your query’s embedding is lined up with each document’s embedding, and it crunches the numbers to see how similar they are. 

Depending on a similarity threshold, the system serves up text chunks with the most relevant document embedding to nail those user queries. Easy, right?

Step 5: Role of Vector Databases

When you’re storing embeddings, you’d typically use specialized databases known as Vector Databases. These databases are crafted to keep vectors in a way that makes searching super easy. Pinecone, Weaviate, and Milvus are some solid examples of these databases.

Now, once you’ve got those spot-on embeddings, it’s time to pull up the corresponding text chunks. These chunks become the context for your chatbot, helping it answer your questions and keep the conversation flowing.

For instance, let’s say you’re creating a custom chatbot for books. You’d take paragraphs from the books, turn them into chunks, and then convert them into embeddings. After that, when a user asks a question about a book, the system can fetch the relevant paragraphs needed to answer their question. 

It’s like having your own book-savvy chatbot!

Step 6: Adjusting Prompts for Language and Tone

When your chatbot talks, it’s important to make sure it sounds right. That’s where prompt tuning comes in. It’s like giving your chatbot a specific style depending on what it’s supposed to do. If it’s selling something, it should sound friendly and convincing. 

If it’s helping customers, it should be more formal and useful. And, to keep things in check, we also make sure the chatbot sticks to the right topics. If it’s there to talk about customer issues, you won’t want it talking about random stuff.

You can choose any of the following ways to adjust prompts for language and tone-

(a) Few-Shot Prompting

When you’re setting up the model, give it examples of how you want the conversation to go in different situations. It learns from those examples and starts responding in a similar way when those situations come up. It’s a super effective way to make the model fit your needs. The more examples you give, the better the model becomes at responding the way you want it to.

(b) Parameter Tuning

When you’re getting your chatbot ready, don’t forget about tuning its parameters. People often overlook this part, but it’s crucial. All chatbots have certain settings you can adjust to control how they behave.

• Temperature: Imagine it like a spice level. Higher values (like 1.0) make the responses more varied and creative, but lower values (like 0.5) keep things more focused. Test it out with a lower value first, and then slowly bump it up to find what works best for you.
• Top-P: This one helps in choosing how many options the chatbot considers when predicting the next word. Setting a threshold (e.g., 0.8) balances between varied and coherent text.
• Maximum Length: This sets a limit on how long the chatbot’s responses can be. If you’re making a simple chatbot, a shorter length might work. But if it’s explaining things, you might need a longer response.
• Frequency and Presence Penalties: These parameters cut down on repeating words. Frequency penalty looks at how often a word appears, while presence penalty only cares if the word is there, not how often.

So, when you’re fine-tuning your chatbot, play around with these settings to get it just right for what you need.

(D) Benefits of using Custom GPT

The custom GPT offers the following advantages-

Let’s dive into the details-

(D.1) Unlocking the flexibility for you with GPT models

When it comes to chatbots, the traditional ones are a bit stuck in their ways. They follow strict rules, need constant attention, and can be a real investment. Not to mention, they aren’t very flexible. But here’s where GPT-4 steps in, offering a whole new level of flexibility. 

Using smart tech like large transformer-based networks, these models get what you’re asking and give personalized responses. No more being tied down by rigid rules – GPT-4 lets your business scale and adapt to all sorts of conversations and surprises.

(D.2) Understanding your queries better

Ever felt like traditional chatbots just don’t get you? GPT-4 changes that game. Trained on big datasets, it picks up on the details and context of what you’re saying, making its responses more on point. Unlike the old-school bots struggling with complex messages, GPT-4 really understands what you’re trying to say, not just putting you into categories.

(D.3) Customization tailored to you

Now, here’s where it gets cool. GPT-4 can be customized super-fast with a bit of prompt magic. Say you’re building a customer support chatbot – toss in some customer service prompts, and voila! It quickly learns how to talk in customer service, picking up the lingo and understanding what’s going on. And the best part? Switching things up is a breeze. Change the context, and your bot adjusts without any hassle.

But with traditional chatbots? It’s a whole training ordeal for each thing you want them to do. They need specific training for each job and context, which is pretty limiting. GPT models, on the other hand, make it easy. 

The context is right there in the prompt, letting your custom knowledge base grow or shrink over time without messing with the model itself. It’s like having a super-flexible assistant that gets better the more you use it!

(D.4) Tailoring Custom GPT for SEO and Marketing

If you’re into SEO and marketing, the latest buzz about custom GPTs might just be your game-changer. OpenAI’s introduction of custom GPTs opens up exciting possibilities, especially for ChatGPT Plus and Enterprise users. 

Let’s dive into how you can leverage this for your SEO and marketing endeavors-

• Determine E-E-A-T for Websites: Let’s say you want to evaluate websites for Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T). With custom GPTs, you can create a Web Quality Analyst in about ten minutes. It uses Search Quality Rater Guidelines and Overview documents as references, making the analysis based on solid knowledge.
• Ranking Factor Analysis: Ever wondered if something is a ranking factor? You can create a GPT specifically for this. For instance, you can build a GPT based on the latest Ranking Factors eBook, giving you quick insights into what impacts rankings.
• Topic Idea Generation: Need inspiration for your next piece of content? Create a GPT that generates 25 headlines and meta descriptions for a keyword or business description. It can even throw in some featured image suggestions.

(D.5) Collaborative Features

Here’s something cool about Custom GPT – now you can save and share the GPTs you create with your friends and colleagues. This collaborative feature not only adds to its usefulness but also promotes a community-driven approach to AI. You can share your GPTs using links, but remember, only ChatGPT Plus users can actually use the GPTs through these links. 

It’s interesting to note that creators won’t have a peek into the conversations users are having with their GPTs. Keep in mind that there’s still some uncertainty about how user data access will work.

(E) Challenges & Drawbacks of Custom GPT

By now you must have understood- What is custom GPT? Now, it is the time to look into the flip side. We have described the challenges and drawbacks of custom GPT in the following table-

Despite these challenges, Custom GPT remains the hot topic among the folks.

Note: Do you know folks are equally crazy about Auto-GPT? If you are curious too, then go through the article “What is Auto-GPT and why is it all over the news?”

(F) Summing Up

In conclusion, you’ve just explored the realm of Custom GPT—a game-changer in the AI landscape. Now armed with the ability to tailor your chatbot’s language, context, and tone, you hold the reins of AI customization. 

From embedding tricks to prompt tuning, you’ve glimpsed the power to sculpt a chatbot that resonates with your specific needs. It’s not just about responses; it’s about personalized, community-driven AI experiences. 

As you navigate this era of AI evolution, Custom GPT stands as your tool for crafting intelligent, adaptable conversations. 

The future of chatbots is in your hands, and it looks remarkably tailored. Thanks for exploring Custom GPT through this article!