How Contextual Retrieval Elevates Your RAG to the Next Level

🤔 Looking to enhance your RAG performance?

Before we dive in, we have some exciting news! Our RAG live course is coming up soon, and as a way of giving back to our amazing community, we're offering you 15% off. Just use this link:
https://maven.com/angelina-yang/maste...

We'd love to see you there! 🎉

In the course, you'll have the chance to connect directly with Professor Mehdi (just like I do 😉 in the videos), and you can even ask him your questions 1:1. Bring your real work projects, and during our office hours, we'll help you tackle your day-to-day challenges.

This course is for:
01 👇
𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: For AI engineers/developers looking to master production-ready RAG systems combining search with AI models.
02 👇
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀: Ideal for data scientists seeking to expand into AI by learning hands-on RAG techniques for real-world applications.
03 👇
𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: Perfect for tech leads/product managers wanting to guide teams in building and deploying scalable RAG systems

In this episode, Mehdi and Angelina introduce the concept of contextual retrieval in Retrieval-Augmented Generation (RAG). We compare it to traditional retrieval methods and discuss how this new approach by Anthropic can significantly reduce retrieval failures by up to 70%. The episode explains the importance of context for Language Learning Models (LLMs) and describes how contextual retrieval enriches document chunks with additional information for improved accuracy. We presents details of the the implementation process, cost considerations, and provide results from Anthropic's experiments showing how contextual retrieval decreases retrieval errors and enhances precision.

Who's Angelina: VP of AI and data, Co-founder of Transform AI Studio, two-time fast.ai fellows under Jeremy Howard, published author.   / meetangelina  

Who's Mehdi: Professor of Computer Science, Co-founder and Chief AI Engineer at Transform AI Studio, NSF fellow, published author.   / mehdiallahyari  

What you’ll learn🤓:

🔎 How contextual retrieval works
🚀 Performance and implementation considerations
🛠 Cost considerations and using prompt caching

✏️ In This Episode:
00:00 Introduction to Contextual Retrieval in RAG
00:29 Understanding Contextual Retrieval
02:59 Challenges with Traditional RAG
05:45 Anthropic's Contextual Retrieval Solution
12:54 Implementation and Cost Considerations
15:44 Experimental Results and Recommendations
18:45 Conclusion and Upcoming Course Announcement

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🖼️ Blogpost for today:

🔨 Implementation:

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Stay tuned for more content! 🎥 Thanks you for watching! 🙌