Unlocking the Power of Retrieval-Augmented Generation (RAG) as Learning Tools

We’ve all seen how ChatGPT brought a paradigm shift in the age of AI in a way we have never seen before. Once the concept of LLMs was successfully implemented on such a large, commercial scale, it was only a given that the technology advancements in this sector would exponentially rise. One such sought after technology is RAG. RAG, or Retrieval Augmented Generation, in the natural processing language (NLP) landscape has emerged as a fascinating concept that combines the strength of retrieval-based systems (a user query that yields documents as an output, or information obtained from external sources related to the query) and generative language models. It is an extension of the generative models providing answers not only contextually accurate but also information rich. (by outsourcing)

UNDERSTANDING RAG

The main working of a RAG can be described in 2 simple steps:

1. Retrieving the information: Imagine you went to the Library of Alexandria and wanted to find materials on a niche topic. The librarian is your retriever who scours the external databases, pulling in relevant books, context, snippets or documents.

2. Generating text from this information: Now a generator simply takes all this information to give out relevant, contextual and coherent responses.

LLMs VS RAG

LLMs are pre-trained AI models that can only provide or rather generate answers based on the existing database that they are ‘trained’ on. The quality of the answers they provide is then typically dependent on the quality/accuracy of data provided to them. RAGs are not so different from LLMs, in that they utilize them too, but after gathering relevant material from external data sources and then feeding this new data to an LLM like GPT to then generate a response.

BENEFITS OF RAG

Responses generated from large language models (LLMs) can pose several challenges, as they are often outdated and limited to a specific information base. This is because, as mentioned above, the answers provided are wholly dependent on the dataset they are trained on. Autonomous RAG reduces these redundancies by using the LLM retrieved content from pertinent content sources (open or close) and then produce a response.

The retrieval-plus-generation process makes RAG systems shine in terms of accuracy, keeping us on the right track by reducing risk of incorrect/misleading content. These systems are particularly helpful in taming the ‘hallucinations’ that LLMs sometimes suffer from (providing plausible but fictional information).

USER CENTRIC LEARNING — ENABLED WITH RAG

User centricity refers to tailoring the product/material to each user, prioritizing the user as every one might have different needs and preferences. In the context of a learning environment, every user has a unique learning curve, requiring varying amounts of time to go through the same material.

RAG comes handy here with its ability to provide tailored responses, be it in terms of gathering relevant materials or answering queries with exact information and not off-topic content(context aware generation). Because users are able to trace the origin of information to the many resources it has been gathered from, there is a certain amount of transparency and trust between the user and the AI model. We will explore more on this topic in the next heading.

RAG SYSTEMS AS A LEARNING TOOL

AutoRAG being the new hot topic of the past few weeks has seen implementation in a lot of ways. One of them of course is as a learning tool.

1. Customized Learning: The thing about content is that in huge amounts, it becomes difficult to find a place to start. RAG systems simplify this by adapting the educational content to individual learner needs, providing personalized feedback and resources. This makes the learning model learner centric rather than the learner having to adapt to the system.
   It reduces the amount of effort needed to start learning something: which is the biggest pillar in learning.

2. Ease of Access: Access to educational content anytime, anywhere, facilitating continuous learning. RAG systems get data from diverse sources, offering exhaustive coverage of topics. You’re no longer limited by your researching/googling abilities, RAG brings it to you.

3. Improved Knowledge Retention: The content provided is engaging and interactive. RAG systems promote active learning and critical thinking. They give a focus to key concepts, reinforcing learning and improving retention.

If we delve deeper into its use case in education, a few points that stand out are tutorial and homework assistance, curriculum development, and the biggest, language learning.

To give a very simple example on language learning:

Scenario: Inquiry about a certain topic/ phrase

1. User query: “I want to know the meaning of Die Daumen drücken in German”
2. Retrieval: RAG retrieves relevant information from sources like textbooks, novels, language forums
3. Generation: Now, using this content, it forms a response — “The meaning of Die Daumen drücken is ‘pressing the thumbs. It is an expression used to wish luck, translating to the English phrase ‘crossing fingers’, depicted by placing a finger across the one next to it.”
4. User Benefit: Gains an understanding of the idiomatic expressions, along with culturally relevant equivalents.

FUTURE DIRECTIONS

Autonomous RAG systems are an emerging and developing technology that can transform the education sector. Ongoing research will bring about technological advancements here as well. In terms of education, we can look forward to and explore its integration with other educational technologies, such as adaptive learning platforms and virtual classrooms. RAG systems can also support lifelong learning by providing resources and support for people of all ages and skills. By combining the strengths of retrieval-based and generation-based models, they offer personalized, and engaging learning experiences.

INTEGRATION IN AISQUARE

AISquare is an innovative platform designed to gamify the learning process for developers. Leveraging an advanced AI system, AISquare generates and provides access to millions, potentially billions, of questions across multiple domains. By incorporating elements of competition and skill recognition, AISquare not only makes learning engaging but also helps developers demonstrate their expertise in a measurable way. The platform is backed by the Dynamic Coalition on Gaming for Purpose (DC-G4P), affiliated with the UN’s Internet Governance Forum, which actively works on gamifying learning and exploring the potential uses of gaming across various sectors. Together, AISquare and DC-G4P are dedicated to creating games with a purpose, driving continuous growth and development in the tech industry.

RAG comes with the ability to reshape and streamline the question retrieval and caching process. It helps in tailoring the learning process and making it more user-focused by getting questions based on the rating of the player and the level of difficulty being exuded in a game. Engagement with the learner is not only enhanced by RAG’s ability to retrieve relevant questions, but also by the gamified experience the platform offers. This improves the retention capacity, improving time taken to go through the material, and makes the learning process fun.

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Author — Aadya Gupta