Accelerated Chatbot Software Delivery

Software development accelerators are end-to-end software development components that software development companies use to build chatbot solutions for clients faster and at a more affordable price.

There is no one-size-fits-all solution to chatbot creation. It depends on a range of factors, including the chatbot type, your industry, hosting providers, and so forth. At Vega IT, we first take time to get to know your business and, based on what we learn, create the solution that works best for you.

Investing in chatbot APIs may seem cheaper at first glance. However, that is not necessarily so. Usually, an out-of-the-box chatbot API comes at a lower initial cost since you’re using existing technology and infrastructure. For instance, for GPT-4, the expense is $0.03 for 1000 prompt tokens. However, that’s how much you will spend for a single request. Consider that, if the request is complex, it may require multiple prompts towards both the GPT-4 API and third-party services. That means your chatbot costs may reach several thousands of dollars per month.

With chatbot solutions based on a software accelerator, you have higher initial costs due to custom development. However, those are one-time expenses that drive long-term success. Once your chatbot is ready, you only pay for the hosting option you choose (a third-party provider or an in-house solution).

The benefits of smart search and intelligent chatbots are multiple for businesses across various industries. They simplify a range of internal business operations, from data entry and analysis and report creation to fraud detection and prevention. Chatbots can also enhance user experiences and inspire customer conversions and loyalty through a range of tactics, from personalized marketing to real-time, 24/7 support.

Some examples of large language models are GPT-3.5, GPT-4, LlaMA, Alpaca, Falcon, LaMDA, NeMO LLM, etc. Each belongs to a different family of LLM models. While all of these models are based on transformer architecture, they vary in the data set on which they are trained. That is precisely why their performance in executing various NLP tasks is never the same. Therefore, we need to be careful when selecting the model to achieve an optimal fit in terms of cost and performance.

Additionally, LLM models can have different sizes. As a rule of thumb, the larger the model, the greater its capacity. For example, Falcon has 180B, 40B, 7.5B i 1.3B models, while LlaMA has 7B, 13B, and 70B models. Of course, the selection of the right LLM size depends on task complexity and impacts resource management. 

Usually, using small or medium-sized LLM models is an optimal choice. On the other hand, large LLM models are applied only when necessary. An example of such a use case involves implementing an intentless policy in a chatbot. Its primary objective is to enable the chatbot to effectively manage a diverse array of user queries, ensuring a valuable and engaging interaction even in scenarios where the user intent is implicit or uncertain.

Note that large LLM models are used as efficiently as possible to help you get the desired functionality at a lower cost. That means we can combine LLMs of different sizes or types to help you get the desired functionality at a lower cost.