Problem

As a team of researchers and developers, we were tasked with building a retrieval augmentation generation (RAG) system for a client in the education industry. The client wanted to use this system to improve their students' learning experience by providing them with personalized content recommendations based on their interests and learning patterns.

However, we faced several challenges while building the RAG system. Firstly, we needed to ensure that the system could retrieve relevant content from a vast database of resources. Secondly, we had to develop an algorithm that could generate summaries of the retrieved content accurately. Lastly, we had to ensure that the generated summaries were easy to read and understand for students at different levels of education.

Learn more about our approach here.

Solution

To address these challenges, we developed a three-step process for building the RAG system:

Retrieval: We used natural language processing (NLP) techniques to retrieve relevant content from a vast database of resources. Our NLP algorithm analyzed keywords and phrases in the student's previous search history and recommended articles based on those keywords.
Augmentation: We developed an algorithm that could generate summaries of the retrieved content accurately. Our algorithm used NLP techniques such as sentence extraction and keyword identification to create concise summaries of each article.
Generation: We ensured that the generated summaries were easy to read and understand for students at different levels of education. We used readability metrics such as Flesch-Kincaid Grade Level and Gunning Fog Index to ensure that the summaries were appropriate for the student's reading level.

Impact

Improved students' learning experience by providing them with personalized content recommendations based on their interests and learning patterns.
Increased engagement and retention rates among students who used the RAG system.
X% increase in the number of students who completed their coursework on time.

"This RAG system has revolutionized the way our students learn. They are more engaged, motivated, and excited about their coursework than ever before."

Team

Team Member #1 - Lead Researcher
Oversaw the development of the NLP algorithms used in the retrieval and augmentation steps of the RAG system.
- Developed a custom keyword extraction algorithm that improved content retrieval accuracy by X%.
Team Member #2 - Lead Developer
Developed the algorithm used in the generation step of the RAG system and ensured that it was optimized for readability at different grade levels.
- Implemented a Flesch-Kincaid Grade Level metric to ensure that generated summaries were appropriate for each student's reading level.

Book: How to build Retrieval Augmented Generation(RAG) for your data?