Build Custom Language Models using RAGs

Presented by

Dear Reader,

Flipped.ai’s weekly newsletter read by more than 75,000 professionals, entrepreneurs, decision makers and investors around the world.

In this week’s newsletter, we will look into how you can build more custom Large Language Models using Retrieval Augmented Generation.

Before, we dive into our newsletter, checkout our sponsor for this newsletter.

Harness the Power of AI

AI’s been here. Now learn where it’s going. The AI Boot Camp from Columbia University serves as an introduction for those just entering tech, and builds on that foundation with specialized AI skills. 

This learning experience is perfect for those looking to upskill and set themselves apart. Upon completion, you will be prepared to lead AI conversations — and initiatives — to bring about key results for organizations. 

No previous programming skills are required to apply.

Why choose the AI Boot Camp?

• Lead the way in all things AI: Learn how to leverage AI and machine learning to automate, solve problems, and drive results. No previous programming experience required.

• Showcase AI skills to employers: Build a portfolio through challenges and team-based projects. Gain access to a network of 250+ employers

• Explore funding options: Find payment plans and other financial resources to meet your needs.

LEARN MORE

Large language models (LLMs) are a type of artificial intelligence (AI) that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, LLMs have some limitations, such as their tendency to hallucinate (generate text that is not factual) and their lack of access to up-to-date information.

Retrieval augmented generation (RAG) is a technique that can address these limitations by augmenting LLMs with external knowledge bases. RAG works by retrieving relevant information from the knowledge base and incorporating it into the LLM's prompt. This allows the LLM to generate more factual and informative responses.

Here are some examples of how RAG can be used in LLMs:

• Question answering: RAG can be used to improve the accuracy of LLM-based question answering systems. For example, a RAG-based question answering system could retrieve relevant Wikipedia articles or other documents from the web and incorporate them into the LLM's prompt when generating an answer. This would help the LLM to generate more accurate and comprehensive answers.

• Summarization: RAG can be used to improve the quality of LLM-generated summaries. For example, a RAG-based summarization system could retrieve relevant sentences from the original document and incorporate them into the LLM's prompt when generating a summary. This would help the LLM to generate more informative and concise summaries.

• Translation: RAG can be used to improve the quality of LLM-based machine translation systems. For example, a RAG-based machine translation system could retrieve relevant parallel sentences from a translation corpus and incorporate them into the LLM's prompt when translating a sentence. This would help the LLM to generate more accurate and fluent translations.

• Creative writing: RAG can be used to inspire and improve the creativity of LLM-based creative writing systems. For example, a RAG-based creative writing system could retrieve relevant poems, stories, or other creative works from a database and incorporate them into the LLM's prompt when generating creative content. This would help the LLM to generate more creative and original text.

Here is an example of how to connect internal data to LLM and do RAG for a question answering system:

1. Prepare your internal data. Your internal data could be in a variety of formats, such as a database of customer records, a knowledge base of product information, or a set of training examples. If your data is not in a text format, you will need to convert it to a text format. You can do this by using a variety of methods, such as using a natural language processing (NLP) library to extract the text from your data or by manually converting your data to text.

2. Choose an LLM. There are a variety of LLMs available, each with its own strengths and weaknesses. For a question answering system, you will want to choose an LLM that is well-suited to answering questions. Some popular LLMs for question answering include BART, LaMDA, and T5.

3. Connect your internal data to the LLM. Once you have chosen an LLM, you will need to connect your internal data to it. This can be done by using a variety of methods, such as loading your data into a database that the LLM can access or by using an API to send your data to the LLM.

4. Implement RAG. To implement RAG, you will need to retrieve relevant information from your internal data when generating an answer to a question. For example, if a user asks the question "What is the capital of France?", you would retrieve the information that the capital of France is Paris from your internal data and incorporate it into the LLM's prompt.

5. Generate text. Once you have implemented RAG, you can generate text by sending a prompt to the LLM. The LLM will generate text based on the prompt and the information it has retrieved from your internal data. In the example above, the LLM would generate the answer "The capital of France is Paris."

Grounding of Language Models and Reducing Hallucinations

One of the key challenges in language model (LLM) generation is ensuring that the generated text is factual and relevant. LLMs are trained on massive datasets of text and code, but they can still generate text that is inaccurate, fabricated, or nonsensical. This is known as hallucination.

Grounding of language models is a technique that can help to reduce hallucinations by ensuring that the generated text is consistent with external knowledge. One way to ground language models is to use a knowledge graph, which is a database of factual knowledge about the world. Knowledge graphs can be used to provide LLMs with context and information about the entities and concepts that they are generating text about.

Another way to ground language models is to use vector databases. Vector databases are specialized databases designed to efficiently store and handle vector data, which is a type of data that represents the semantic meaning of text. Vector databases can be used to retrieve documents and data that are semantically or contextually related to the query.

By grounding language models in factual knowledge, we can help to ensure that the generated text is more accurate, informative, and reliable. This is important for a variety of applications, such as question answering, summarization, and translation.

Here are some specific examples of how grounding language models can help to reduce hallucinations:

• If a user asks an LLM the question "What is the capital of France?", the LLM may be able to generate the correct answer, "Paris", even if it has never been trained on that specific question. This is because the LLM has been trained on a massive dataset of text and code that includes information about the world, such as the fact that Paris is the capital of France.

• If a user asks an LLM to summarize a news article, the LLM may be able to generate a summary that is accurate and informative, even if it has never been trained on that specific article. This is because the LLM has been trained on a massive dataset of text and code that includes information about a variety of topics, such as current events.

• If a user asks an LLM to translate a sentence from one language to another, the LLM may be able to generate a translation that is accurate and fluent, even if it has never been trained on that specific sentence. This is because the LLM has been trained on a massive dataset of text and code that includes information about a variety of languages.

Overall, grounding language models is a powerful technique that can help to improve the performance and reliability of LLMs on a variety of tasks.

Overall, RAG is a powerful technique that can be used to improve the performance of LLMs on a variety of tasks. As LLMs become more widely adopted, we can expect to see RAG play an increasingly important role in the development of AI-powered applications.

Thank you for being part of our community, and we look forward to continuing this journey of growth and innovation together!

Best regards,

Flipped.ai Editorial Team