What is RAG? Understanding Retrieval Augmented Generation

Simulating the way humans communicate, understanding phrases and context, and generating text has been a long-standing goal in AI research. In recent months, the rise of Large Language Models and generative AI has captured the market's fervent interest. Yet, amid this enthusiasm, numerous companies are grappling with concerns regarding the data used by these advanced language models during the response generation process. The apprehension stems from the fact that these models are trained using historical data, often sourced from unknown origins, raising legitimate doubts about their authenticity and reliability.

?Within this context, a particular acronym has begun to surface with increasing frequency in the discourse on AI: RAG

However, what exactly does this frequently mentioned term mean?

In this article, I'm going to break down what it means and why it's important.

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The Challenge

The potential of Large Language Models and Generative AI to understand, transform, and generate texts is already know and has transformed society and business. However, consider a scenario where an investment analyst needs to provide up-to-date and accurate information about a particular company's stock performance to a client. Using a pure generative model, the analyst can provide a response based only on historical data and would be missing the most up-to-date data. Or, to talk about another industry, imagine a lawyer preparing a defense strategy for a client using LLM to generate legal arguments based on established legal principles. Now imagine if new decisions and precedents in similar cases occur in a more recent period, not yet known by LLM. The lawyer would be missing good arguments in the strategy

And mere fine-tuning is insufficient. If your data evolves over time, even a finely-tuned model's accuracy may decline, necessitating additional expensive and time-consuming data labeling, continuous quality monitoring, and repeated fine-tuning

These challenges are being addressed with a framework called Retrieval Augmented Generation, or RAG.?

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What is Retrieval Augmented Generation (RAG)?

The Retrieval Augmented Generation, or RAG, is a technique that combines the power of pre-trained large language models with the ability to retrieve information from external sources. In essence, RAG is a framework that bridges the gap between pure use of generative AI models and those use cases where the company needs to use data from a predefined dataset. It combines both approaches, enhancing the capability to produce coherent and contextually appropriate responses.

Practical example

Given our discussion on AI and conversational interactions, I will ask LLM about the concept of Tokens, a term frequently employed in this context. Let's see LLM's response:

A token is a versatile term with various meanings across different contexts. In natural language processing, a token represents a meaningful unit of text, like words or punctuation. In the cryptocurrency world, tokens are digital assets issued on blockchains to represent ownership, assets, or utility. Tokens are also used in security and authentication, as well as in programming to represent small units of code. They find applications in gaming, mathematics, board games, transportation, and many other fields, often denoting a discrete and essential unit or element. In summary, a token can refer to discrete units in different domains, each with its specific use and meaning, making it a versatile concept that adapts to its context

Now, the same question if I were using RAG to contextualize the domain I'm referring to:

In Natural Language Processing (NLP), a token is the fundamental building block of text. It represents the smallest unit into which a piece of text, such as a sentence or document, can be divided. Tokens are typically words, where each word in a sentence is considered a separate token. For example, in the sentence "I love NLP," there are three word tokens: "I," "love," and "NLP." However, tokenization can also involve subword, character, or even sentence-level units, depending on the specific task and the tokenization approach used. Tokenization is a crucial preprocessing step in NLP, as it transforms raw text data into a format that can be readily processed and analyzed by NLP models.

Main benefits of RAG

• Accuracy: The use of a RAG approach can significantly enhances the accuracy of the answers generated by the LLM by considering real-time and external information. It reduces the chances of hallucination
• Contextual Relevance: RAG can also excel at delivering contextually relevant answers, making it ideal for complex, data-driven tasks. Combining LLM with RAG enables the creation of domain-specific applications
• Reduced Biases: The retrieval mechanism allows for immediate access to diverse and unbiased data sources, minimizing the perpetuation of AI biases.
• Cost Reduction: Eliminating the need for frequent model training leads to decreased operational staff and expenses?

Risks of RAG

The risks are very similar to the risks of using LLM. In other words, biases in generated content, inaccurate information, privacy concerns, ethical dilemmas, etc. However, talking specifically about RAG, I would highlight points to keep an eye on:

• ?Privacy Concerns: As important as the accuracy of responses is security and governance. Access to external data sources could potentially raise privacy issues if sensitive information is retrieved.
• Data Quality: Inaccurate or biased data sources can lead to misinformation.

RAG enhances LLM capabilities by integrating real-time external knowledge, ensuring current and contextually relevant information. This integration can substantially enhance user experiences and information accuracy, providing a reliable solution for modern AI applications.