Narrowing down LLMs for your Business Needs
You already know the cool things AI models can do. The challenge is to identify how these models can fit into your operations, taking into account your business tech stack. I will explain the most common (and uncommon) solutions LLMs can bring to your business.
The premise of LLMs is to generate content, taking human language as input and returning data such as text, images, or code. While the tools currently available are very effective in responding accurately, they haven’t been fine-tuned with your specific data, and this represents the first type of LLM application: Chatbots.
Chatbots
Chatbots have been implemented in web applications for decades, but after the release of GPT-3.5, the public realized the next level of sophistication in these models’ responses. The introduction of these models opened the doors for many businesses to replace their old-school bots, adding more complexity and nuance to their customer interactions.
Chatbots can be implemented across multiple areas of your business, especially in customer relationship management. The difference between a chatbot and a powerful chatbot is the data used to fine-tune your model. If you’re unfamiliar with fine-tuning, it’s the process of adding another layer of training to a pre-trained model.
Since creating an LLM from scratch requires a significant amount of hardware resources and software expertise, businesses can rely on open-source or private pre-trained models and eventually train them with their business data to answer specific business-related questions.
From a business perspective, customer support is a key component in the lifetime value a client brings to their business. That’s why implementing this solution into your CRMs is crucial to stay competitive and deliver a satisfactory experience to your customers.
Training your ‘chatbot’ with your data opens up multiple ways to handle specific customer situations within your processes. However, I don’t want to leave you with only one approach but to introduce you to an approach that has not been widely discussed. Let’s talk about the second type of LLM application for your business: Custom models for internal usage.
Task-Performing Chatbots:
Having a conversation with any kind of file that your business has will allow you to see the big picture and possibly evaluate scenarios suggested by the AI that you hadn’t considered before. By enabling your model to access files and internal resources within your company, you can maximize the potential of this technology in your business.
A great example is this: let’s say you are using a CRM. Generally, CRMs have APIs that allow you to integrate CRM functionalities within your programs. Your program can be an LLM model that performs multiple tasks like uploading a new list of leads to your CRM from your social media channels and paid advertising. You can simply instruct your model to upload the leads to your CRM, and it will do it. Now imagine that you can also direct your LLM to generate a report of your outreach campaigns and compare it to previous reports.
Delegating functions to your LLM can significantly impact your business. This solution requires implementing features that the best models on the market offer, such as function calling and knowledge retrieval. These types of LLM applications are particularly relevant in business contexts because they offer immediate value in saving time and money.