Your Cheat Sheet to LLMs

💡AI TRIVIA

Retrieval-Augmented Generation (RAG) boosts the performance of a large language model by using a trusted knowledge source that isn't part of its initial training data. Large Language Models (LLMs) learn from a wide range of information and use millions of settings to create unique responses for tasks like answering questions, translating languages, and completing sentences.

🍎 Quick Refresher

RAG, or retrieval augmented generation, combines a user's question with external info to form a new prompt for models like GPT-4 or Llama 2. This tweak boosts response accuracy and relevance.

While these models are great with language, they can't always be trusted. They can't access private data or anything beyond their training and may "hallucinate," making up answers. RAG addresses this by providing the necessary info, helping models tackle questions outside their scope and cutting down on those made-up answers.

For example, a model trained only on public data can't handle questions about a company's internal memos and might make stuff up if asked. RAG lets you supply document parts, giving the model the right context to provide accurate answers.

🛠 Leveraging AI For Your Business: How to Use RAG For Your Business?

Here is the “Hows” of Rags. Fast things you can apply and use directly to make the most out of RAGs in LLMs.

Vector Search

To make an RAG app effective, it needs to fetch accurate info. Picture sorting through tons of documents to find the gems. That's not easy! Vector Search steps in to help find the right text.

In an RAG setup, the "embedding model" turns each text into a number string that captures its essence. It does the same for the user's question. By comparing these number strings, we can find texts that closely match the question.

These vectors dig into the meaning of the text. So when we search for the right ones, we're really hunting for meaning. This clever method grabs the best-matching texts and pairs them with the user's question, sending it to the LLM.

Make a Database

Embedding models create vectors that often find a home in a special type of database just for them. These vector databases are the superheroes of efficiency when it comes to storing and pulling out vector data. Just like your regular databases, they handle things like permissions, metadata, and keeping data safe and sound. This ensures that information is both secure and neatly organized. Plus, they're designed to quickly index new texts, so everything's ready to roll in no time.

Add Definite Context

Large language models (LLMs) can't handle endless text. They have a "context window" limit, so when you're setting up a Retrieval-Augmented Generation (RAG) system, keep everything within that limit. Stuffing in too much text? Big mistake. Some LLMs handle more text, even short book-sized chunks. But more isn't always better. LLMs often focus on the start and end, ignoring the middle. This "lost in the middle" issue means careful text selection and organization is crucial for effective prompts.

💡 Identifying Issues

LLMs are pretty smart, giving clear answers and picking up on nuances. But if they don't know something, they might make up answers. They only know what they're trained on and can't access private company info.

For better accuracy, give LLMs the right info from the start. A few pages of relevant docs can help a lot. A vector database can automate this process, feeding the model info without extra effort from you.

Using RAG with Vector Search does add some data steps, but it's worth it. RAG mixes info for sharper, more personalized responses than retrieval alone.

🌐Use Cases

RAG gives language models the extra boost of context-specific info they need when they can't come up with it on their own. This opens doors to many applications that would be tough or even impossible with language models alone.

Question-And-Answer

In question-answering systems, RAG shines when you need to "chat" with documents. Think of it like asking questions about HR policies or checking real-time financial reports. With RAG, information is pulled together and shared in a way that feels like a natural conversation. For example, a big e-commerce company uses Databricks for a RAG setup so their HR folks can easily search through tons of employee policy docs.

Customer Service

For customer service, RAG makes things smoother by helping support teams give more tailored and informed answers. This means happier customers, faster replies, and more efficient problem-solving. Many businesses use this kind of RAG "internal copilot" to help their teams work better.

Content Generation

When it comes to content creation, RAG is like having a smart assistant that helps draft communications, such as sales emails, using the latest data and context. This ensures customer outreach is personalized and up-to-date. One Databricks client uses RAG to craft email replies that blend external product info with customer details.

Code Assistance

In code assistance, RAG boosts code completion and Q&A systems by smartly fetching details from code bases, docs, and external libraries. This leads to better code generation and more relevant responses than what you'd get with just language models.

🏬 Industry Examples

Retail. Chatbots for customer service, creating marketing content, handling customer questions, analyzing email escalations, and summarizing conversations.

Medical. Managing documents, summarizing diagnostics, and querying patient files.

Technology Development. Automating customer service, addressing customer questions, summarizing conversations, creating content, and managing emails.

Travel and Hospitality. Automating customer service, analyzing feedback, creating content, and managing emails.

💡 Pro Tip of the Day: Have a Database Ready

To use RAG with Vector Search, we first have to put our unstructured text data into a vector database. There are several ways to go about this, and it's crucial to experiment with different methods to find the best fit for your needs. Preparing data isn't a one-off task, as a vector database needs regular updates to ensure the information is current and accurate. One great advantage of RAG is the ability to keep the vector database fresh without having to change the LLM weights over time.

🔥 Hot Takes

In a society where we value speed and authenticity, can we patent our AI processes?

🤔 Intrigued by AI's Potential, But Unsure of the Next Step?

We get it! This issue might have you brimming with possibilities, but where do you begin? The AI StandUp is made with ❤️ by The BrainTrust and we are here to bridge the gap. Head over to our website, or simply reply to this email with your specific questions. Let's turn your AI curiosity into a game-changing reality.

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