If you need inspiration, check out the list of our 9 best chatbot platforms.
Step 3: Collect data
If you want to perform advanced query or fine-tuning, you will need to collect the data set that will inform your chatbot.
For example, if you want to relieve your customer service team by building a bot that mimics their techniques, you can collect transcripts of successful customer service calls.
Step 4: Customize and Integrate
The most exciting part? Build your chatbot in gpt.
Your chatbot platform will allow you to customize the spain mobile phone number actions your chatbot performs, the tone or personality it emulates, and individual conversation flows.
You can even ask your chatbot to perform a certain task and it can carry it out autonomously.
You will also have to integrate your chatbot with the necessary information sources. For example, if you want it to explain your products, the gpt chatbot must be connected to your website and your product catalog.
Step 4: Deploy and test
You'll probably want to deploy your bot on a website, but it can also be useful to deploy it on other channels. Depending on your purpose, you may want to set it to the most popular messaging channel of your customers, or the platforms most used by your employees.
Once the chatbot is created, you or your team will have to test different situations and iterate on it.
Training a gpt model
illustration of a computer and piles of papers.
If you are interested in creating your own gpt chatbot, it will be helpful to understand how the gpt model was created.
A gpt model is born from pre-training and can be further specialized with fine tuning. However, you can also build a custom gpt chatbot that doesn't involve fine tuning, which is an intensive process that can quickly get expensive.
Previous training
Pre-training is a time- and resource-intensive process that, at the moment, can only be carried out by well-funded companies. If you are building your own chatbot in gpt, you won't have to pre-train it.
Pre-training occurs when a development team trains the model so that it is able to accurately predict the next word in a human-sounding sentence. Once the model has been trained with a large amount of text, it can more accurately predict which words should follow which in a sentence.
