RAG for Dummies: Ace Your AI Exams with Matillion

Wait - What is RAG?

Imagine your large language model (LLM) taking an exam blindfolded, relying on best guesses or sometimes providing no answer at all. Sounds frustrating, right? But what if we told you there's a game-changer called Retrieval-Augmented Generation (RAG) that opens up the exam like an encyclopedia, allowing your LLM to access a wealth of information for accurate and rapid answers? In this blog, we'll guide you through the basics of RAG and how Matillion can help you transform your AI game.

Currently, Your LLM is Flying Blind:

Your LLM operates like a student taking an exam without access to any resources, offering educated guesses or, at times, leaving questions unanswered.

Enter RAG - Your Open-Book Exam:

With Retrieval-Augmented Generation (RAG), your LLM gets the advantage of an open book, allowing you to provide it with a comprehensive set of information for quick and accurate responses.

What's Preventing You from Acing the Test?

1. Didn't Realize You Could Bring a Book (Didn't Know About RAG):

Many are unaware of the existence of RAG and its potential. You may not even know how to get started incorporating RAG into your data pipelines.

2. Been Briefed on Exam Format, But Don't Have a Book (Don't Know How to Put RAG Into Action):

If you're familiar with RAG but lack the knowledge of how to implement it effectively, you're essentially aware of the exam format but don't have the necessary resources to succeed.

3. Provided a Bad Book (Implemented RAG Without Proper Data Preparation):

Even if you've implemented RAG, poor data preparation can lead to subpar AI outcomes. Just like offering an outdated book without a strong table of contents, bad data won't yield the desired results.

Get the RIGHT Answers with RAG

Less Ignorance. More Bliss:

Matillion is here to demystify RAG for you. We provide resources to help you understand the value of incorporating RAG into your workflows, turning ignorance into bliss.

Get Started. Equipped with the Right Tools:

Matillion's RAG component empowers you to set up the RAG process, extending the knowledge of LLMs with your data loaded within vector stores. Activate RAG to get contextualized answers with ease.

Enterprise Data. Ready for RAG:

The core of successful RAG lies in strong data. Utilize Matillion's RAG component to integrate data from your vector databases like Pinecone or pg_vector. Build pipelines to clean and transform your governed enterprise data, preparing it for RAG.

Let’s see how Matillion implemented their first RAG use case for great impact in customer success

Let's delve into a real-world scenario where Matillion's Retrieval-Augmented Generation (RAG) component proves its mettle. Imagine a scenario where the goal is to enhance data productivity within the realm of customer support, providing swift and accurate responses to inbound support cases. In this Matillion case study, we'll explore how RAG, coupled with Matillion's Data Productivity Cloud, was employed to create a robust pipeline, utilizing vector databases and large language models (LLMs) for automated, high-quality responses. The success of this use case illustrates the tangible benefits of incorporating RAG into your workflows and how Matillion empowers data engineers to achieve impressive AI outcomes visually and without the need for specialized skills or code. Let's dive into the details of this transformative use case.

Matillion Case Study: Enhancing Data Productivity with AI in Customer Support

Goal of Use Case:

Provide satisfactory answers to inbound support cases and solve support issues using AI.

What was Built:

• Developed a RAG pipeline using Data Productivity Cloud.
• Loaded support case history and Matillion documentation into the vector data store to provide context and fine-tune the LLM
• Utilized the vector data store and LLM for automated high-quality responses
• No specialized AI skills are required—built by a data engineer visually and without code
• Seamless integration with source systems for simple, scalable runtime and maintenance

Results Received:

Ongoing runtime, maintenance, and data operations simplified—ensuring scalability and efficiency. Seamless integration with destination systems, as draft answers are sent back to Salesforce using Matillion’s reverse ETL, entering the support agents' queue.

Ready to ace your AI exams?

Register now for our event, “AI. The Future of Data Engineering,” to see live demos of our RAG component and learn more about how to engage with AI in the data engineering space.