Retrieval Augmented Generation (RAG): The Ultimate Guide

Retrieval-Augmented Generation (RAG) combines the strengths of traditional information retrieval systems (such as databases) with the capabilities of generative large language models (LLMs). By integrating external knowledge with its skills, the AI can produce answers that are more accurate, up-to-date, and relevant to specific needs. If you want to understand the basics of RAG take a look at this article.

Why is it called RAG?

Patrick Lewis, the primary author of the paper that first presented RAG in 2020, named the acronym that currently characterizes an expanding array of techniques utilized in countless papers and multiple commercial services. He believes these represent the forthcoming evolution of generative AI.

Patrick Lewis leads a team at AI startup Cohere. He talked about how they came up with the name in an interview in Singapore where he shared his ideas with a conference of database developers in the region.

“We always planned to have a nicer sounding name, but when it came time to write the paper, no one had a better idea.” Lewis said.

Since its publication, hundreds of papers have cited the Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks paper, building upon and expanding its concepts to make it a significant contribution to ongoing research in this field.

In 2020, the paper was published while Lewis was pursuing his doctorate in NLP at University College London and working for Meta at a new AI lab in London. The team aimed to enhance the knowledge capacity of large language models (LLMs) and developed a benchmark to measure their progress.

Drawing inspiration from previous methods and a paper by Google researchers, the team envisioned a trained system with an embedded retrieval index that could learn and generate any desired text output, according to Lewis.

What is Retrieval Augmented Generation?

In layman's terms, it's an AI framework where the system first hunts down the relevant information from vast data reserves and then employs this data to formulate responses with precision and insight. RAG optimizes the output of a large language model by referencing an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences.

RAG extends the capabilities of LLMs to specific domains or an organization's internal knowledge base without needing to retrain the model. This cost-effective approach ensures that LLM outputs remain relevant, accurate, and useful in various contexts.

Why You Need to Know About Retrieval Augmented Generation?

It promises to drastically enhance the usability of LLMs. LLMs are powerful tools, but their integration into applications can be challenging due to issues with accuracy and transparency. RAG addresses these problems by connecting an LLM to a data store, ensuring that responses are both accurate and verifiable. it can be used by nearly any LLM to connect with practically any external resource.

Neural networks known as Large Language Models (LLMs) are frequently assessed based on their number of parameters. These parameters encapsulate broad patterns of language use, enabling LLMs to construct coherent sentences.?

This encoded understanding, termed parameterized knowledge, allows LLMs to generate rapid responses to general queries. However, this approach has limitations when users require in-depth information on specialized or up-to-date subjects.

Problems with Current LLMs

1. Out-of-Date Information - LLMs often provide answers based on outdated data. For instance, ChatGPT might state it only has knowledge up to September 2021, leading to obsolete or incorrect answers, especially in fields like scientific research.

1. Lack of Source Transparency - LLMs do not provide sources for their information, requiring users to blindly trust the accuracy of the answers.

1. Trust - You might not trust LLMs answers on specific topics or tasks.

How RAG Solves These Problems

RAG connects an LLM to a data store, allowing it to retrieve up-to-date information when generating responses. For example, if you want to use an LLM to get current NFL scores, RAG would enable it to query a real-time database of NFL scores and incorporate this information into its response. This approach ensures the accuracy of the information and provides a clear source.

How Does Retrieval Augmented Generation Work?

RAG systems operate in two phases: Retrieval and Content Generation.

Retrieval Phase: Algorithms actively search for and retrieve relevant snippets of information based on the user’s prompt or question using algorithms. This retrieved information forms the basis for generating coherent and contextually relevant responses.

Content Generation Phase: After retrieving the relevant embeddings, a generative language model, such as a transformer-based model like GPT, takes over. It uses the retrieved context to generate natural language responses. The generated text can be further conditioned or fine-tuned based on the retrieved content to ensure it aligns with the context and is contextually accurate. The system may include links or references to the sources it consulted for transparency and verification purposes.

Take a look at how Valere approaches RAG: https://youtu.be/_wXTYESaAcw?

How to Implement Retrieval Augmented Generation

First, you need a Retrieval Engine. The Retrieval Engine is responsible for searching and ranking relevant data based on a query. It scours extensive databases and indexes to find the most pertinent information that can support and enrich the response generated by the system.

Next, the Augmentation Engine takes the top-ranked data from the Retrieval Engine and adds it to the prompt that will be fed into the Language Learning Model (LLM). This step ensures that the LLM has access to the latest and most relevant information.

Finally, the Generation Engine combines the LLM's language skills with the augmented data to create comprehensive and accurate responses. It synthesizes the retrieved information with the pre-existing knowledge of the LLM to deliver precise and contextually relevant answers.

RAG Components

Data Indexing: The first step involves organizing external data for easy access. This can be achieved through various indexing strategies that make the retrieval process efficient.

Different strategies include:

• Search Indexing: Matches exact words.
• Vector Indexing: Uses semantic meaning vectors.
• Hybrid Indexing: Combines search and vector indexing methods for comprehensive results.

Input Query Processing: This step fine-tunes user queries to ensure they are compatible with the search mechanisms. Effective query processing is crucial for accurate and relevant search results.

Search and Ranking: In this phase, the system finds and ranks relevant data using advanced algorithms. These algorithms assess the relevance of data to ensure the most pertinent information is retrieved.

Prompt Augmentation: Here, the retrieved top-ranked data is incorporated into the original query. This augmentation provides the LLM with additional context, making the responses more informed and accurate.

Response Generation: Finally, the LLM uses the augmented prompt to generate a response. This response combines the LLM's inherent knowledge with the newly retrieved external data, ensuring accuracy and relevance.

Retrieval Augmented Generation Tutorial

If you want to learn more, take a look at this five-minute tutorial about Retrieval Augmented Generation:

https://youtu.be/Sub89KWBs84?

Keep reading our guide here: https://www.valere.io/blog-post/retrieval-augmented-generation-rag-ultimate-guide/120