Create Advanced LLMs with Retrieval-Augmented Generation for Enhanced Proficiency | Authored by John Adeojo | August 2023

Create Advanced LLMs with Retrieval-Augmented Generation for Enhanced Proficiency | Authored by John Adeojo | August 2023

Introduction:

Introduction:

Discover how retrieval augmented generation (RAG) can revolutionize your LLMs (Language Learning Models) by incorporating a knowledge base. The limitations of models like Chat-GPT arise from insufficient training data and the tendency to fabricate information. To overcome this issue, we explore two options. The first involves training or fine-tuning the model on up-to-date data, which can be costly and time-consuming. Alternatively, RAG methods allow us to equip large language models with access to an updated knowledge base, making implementation easier and more cost-effective. In this article, we delve into implementing RAG with your OpenAI model and analyze its proficiency in answering questions related to the Russia-Ukraine conflict of 2022 using a Wikipedia knowledge base. To accomplish this, we utilize OpenAI API, Haystack by Deepset, sentence transformers, and the transformers library from Hugging Face. Grab your OpenAI API key and let’s enhance your LLMs!

Full Article: Create Advanced LLMs with Retrieval-Augmented Generation for Enhanced Proficiency | Authored by John Adeojo | August 2023

How Retrieval Augmented Generation Can Take Your LLMs to the Next Level

In the world of natural language processing, the Chat-GPT model developed by OpenAI has gained significant attention for its ability to generate human-like text. However, it has its limitations when it comes to practical business use cases, particularly outside of code generation. One of the main challenges is the model’s reliance on training data, which can cause it to produce inaccurate or hallucinated responses.

If you were to ask the Chat-GPT model questions about events that occurred after September 2021, you would likely receive unhelpful and incorrect answers. This limitation can hinder the model’s effectiveness and reliability for real-world applications. So, how can we overcome this challenge and enhance the performance of language models like Chat-GPT?

Option 1: Train or Fine-tune the Model on Up-to-Date Data

One approach to addressing this issue is to train or fine-tune the model using recent and relevant data. However, this method comes with its own set of challenges. Fine-tuning or training a language model can be costly and time-consuming. Additionally, preparing the necessary data sets for training can require a significant amount of effort and resources, making this option impractical for many businesses.

Option 2: Embrace Retrieval Augmented Generation (RAG) Methods

An alternative solution to improve the performance of language models like Chat-GPT is to leverage retrieval augmented generation (RAG) methods. RAG methods allow you to integrate a knowledge base into the language model, giving it access to up-to-date information. By doing so, you can enhance the model’s ability to provide accurate and relevant responses.

Implementing RAG methods is much more cost-effective and easier to implement compared to training a model from scratch or fine-tuning. In this article, we will explore how you can leverage RAG with your OpenAI model to enhance its capabilities. To demonstrate the effectiveness of this approach, we will conduct a short analysis of the model’s ability to answer questions about the Russia-Ukraine conflict of 2022 using a Wikipedia knowledge base.

Getting Started with RAG

Before you can begin using RAG, you will need an OpenAI API key, which you can obtain from their website. Additionally, we will be using the Haystack framework by Deepset, an open-source tool that provides APIs for building applications on top of large language models. We will also leverage sentence transformers and the transformers library from Hugging Face, which offer powerful tools for text analysis and natural language processing.

Conclusion

Retrieval augmented generation (RAG) methods offer a practical and effective way to enhance the performance of language models like Chat-GPT. By integrating a knowledge base, these methods enable the model to access up-to-date information and provide accurate responses. This approach is more cost-effective and easier to implement compared to training or fine-tuning a model from scratch. By leveraging RAG with your OpenAI model, you can unlock its full potential and improve its ability to handle real-world business use cases. Start exploring RAG methods today and take your language models to the next level.

Summary: Create Advanced LLMs with Retrieval-Augmented Generation for Enhanced Proficiency | Authored by John Adeojo | August 2023

Retrieval Augmented Generation (RAG) is a method that can enhance your LLMs (Large Language Models) by integrating a knowledge base. While models like Chat-GPT have limitations due to their training data and tendency to generate irrelevant information, RAG methods offer a solution. Instead of costly and time-consuming model training or fine-tuning, RAG provides access to an up-to-date knowledge base, making implementation easier and more affordable. In this article, the author demonstrates how to leverage RAG with OpenAI models using a Wikipedia knowledge base. The tutorial requires an OpenAI API key and utilizes the Haystack framework, sentence transformers, and the transformers library.

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