In this lesson we will explore how LangChain, Deep Lake, and GPT-4 can be used to develop a sales assistant able to give advice to salesman, taking into considerations internal guidelines.
Introduction
This particular article provides an in-depth view of a sales call assistant that connects you to a chatbot that understands the context of your conversation. A great feature of SalesCopilot is its ability to detect potential objections from customers and deliver recommendations on how best to address them.
The article is a journey that reveals the challenges faced and the solutions discovered during the creation of a the project. You'll learn about the two distinct text-splitting approaches that didn't work and how these failures paved the way for an effective solution.
Firstly, the authors tried to rely solely on the LLM, but they encountered issues such as response inconsistency and slow response times with GPT-4. Secondly, they naively split the custom knowledge base into chunks, but this approach led to context misalignment and inefficient results.
After these unsuccessful attempts, a more intelligent way of splitting the knowledge base based on its structure was adopted. This change greatly improved the quality of responses and ensured better context grounding for LLM responses. This process is explained in detail, helping you grasp how to navigate similar challenges in your own AI projects.
Next, the article explores how SalesCopilot was integrated with Deep Lake. This integration enhanced SalesCopilot's capabilities by retrieving the most relevant responses from a custom knowledge base, thereby creating a persistent, efficient, and highly adaptable solution for handling customer objections.
By the end of this lesson, you'll learn how to utilize LLMs, how to intelligently split your knowledge base, and integrate it with a vector database like Deep Lake for optimal performance.
In the next lesson, we’ll see how to leverage both LLMs and image generation models to write a creative children’s picture book.