Announcing our Blog with AI Tips and Tricks

Mar 2, 2024 | Vizaport

Blog

Our New Blog Site

At Vizaport, we are thrilled to announce the launch of our new blog site, dedicated to unraveling the complexities of training custom AI models for businesses. As the realm of artificial intelligence continues to evolve, our primary goal is to empower individuals and organizations with valuable insights, tips, and tricks that demystify the process of harnessing AI’s full potential.

AI Tips and Tricks

In our forthcoming articles, we will hone in on the intricacies of using embeddings and Retrieval Augmented Generation (RAG) for training Language Model Models (LLMs). Whether you’re a seasoned professional or venturing into the realm of AI for the first time, we understand that the path to mastering these techniques can be daunting. Our focus is to provide practical insights, troubleshooting tips, and strategies for overcoming hurdles in the dynamic world of AI model training. From the nuances of fine-tuning to addressing common pitfalls, our blog aims to be your guiding companion in this ever-evolving space.

Introducing our AI Training Doc for RAG

To kickstart this journey, we’ve curated an AI Training page that serves as an evolving guide that will document our learnings of training RAGs with different types of files and structures. While the examples on this page utilize Vizaport’s loader, rest assured that the underlying concepts and file structures are universally applicable, extending beyond specific tools. Whether you’re leveraging LlamaIndex or any other RAG, the fundamental principles outlined in our AI Training page will guide you in preparing, organizing, and optimizing your data effectively.

Examples

Some of the examples for tips and tricks of AI training is how the data is structured. How data is organized and put into models make a big difference in the AI accuracy score. We cover the following types of data structures in our AI training, and we will continue to evolve these learnings and share with the community.

 

Structured Data

Spreadsheets, JSON files, XML files, etc.

Structured data refers to organized and formatted information that is presented in a clear and systematic manner, typically within a database or spreadsheet, or formats like JSON and XML. In the context of Language Model Models (LLMs), structured data proves to be invaluable. Unlike unstructured data, which lacks a predefined format, structured data provides a well-defined framework that facilitates easy interpretation and analysis by LLMs. This structured format allows the model to discern patterns, relationships, and connections within the data, enhancing its ability to generate accurate and contextually relevant responses. The organized nature of structured data enables LLMs to navigate and understand information more effectively, contributing to improved performance in tasks such as language comprehension, natural language processing, and generating meaningful outputs. Ultimately, structured data acts as a cornerstone for optimizing the capabilities of LLMs, enabling them to deliver more precise and contextually aware responses.

 

Unstructured Data

Documents, PDFs, text files, etc.

Unstructured data refers to information that lacks a predefined format or organization, often existing in the form of free-text documents, images, audio recordings, or other content without a rigid structure. When it comes to Language Model Models (LLMs), dealing with unstructured data presents both challenges and opportunities. LLMs are designed to understand and generate human-like language, making them adept at processing and interpreting unstructured data. However, the lack of a predefined structure in unstructured data can pose difficulties for LLMs in extracting meaningful patterns and relationships. Despite these challenges, LLMs excel in handling unstructured data by leveraging their ability to learn from diverse and complex information sources. Through continuous training and exposure to varied unstructured data, LLMs can enhance their language comprehension, enabling them to generate more contextually relevant and coherent responses across a wide array of applications, from text summarization to conversational interactions.

Good luck in your AI Adventures

We hope that our blog will be a valuable resource for you and your AI efforts. If there’s anything we can do to help, feel free to contact us.

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