Driving Down Cost in AI Workflows
Large Language Models (LLMs) like OpenAI's GPT-3 have revolutionized various business and personal productivity tasks. By automating complex processes, these advanced AI tools have significantly reduced the time and resources required to complete various tasks, leading to enhanced productivity and innovation. This article explores how developers can augment LLMs with more cost effective tools for simple tasks to streamline AI workflows, drive down costs and increase efficiency.
Utilizing LLMs in AI Development
In AI process development, it's common to rely on LLMs for numerous stages of a process. For instance, when building an internal knowledge base from various sources like company PDFs, public websites, and intranets, developers often use LLMs for a range of tasks including data scraping, extraction, chunking, embedding, retrieval, and the customer interface. LLMs excel in these roles due to their ability to process and understand large volumes of unstructured data, such as social media posts or customer reviews.
The Versatility and Limitations of LLMs
While LLMs are incredibly versatile, capable of reading, writing, coding, and even drawing, it's important to recognize that they may not always be the most cost-effective solution for every tasks. Lower-level tasks such as data scraping and extraction can often be handled by more specialized, lightweight tools. Employing these tools for the initial stages of data processing can significantly reduce costs while reserving LLMs for more complex tasks like high-level reasoning and data sorting.
Example Use Case
ExtractorAPI is a prime example of a specialized tool designed for initial stages of data processing. It's a lightweight model specifically developed for text extraction, suitable for everything from knowledge aggregation to advanced AI use cases. Developers can utilize ExtractorAPI for extracting data from websites or internal documents like PDFs, offering a more cost-effective and efficient way to process initial data before handing it over to more advanced LLMs for further analysis and reasoning.