RAG: The Future of LLMs

Retrieval Augmented Generation (RAG) is a cutting-edge technology that enhances the effectiveness of Large Language Models (LLMs) such as OpenAI's ChatGPT and Anthropic's Claude. Despite their remarkable capabilities, LLMs are fraught with challenges that render them unsuitable for specific tasks, particularly those requiring up-to-date or domain-specific information. RAG addresses these issues, thereby boosting the performance of Generative AI (GenAI) applications. This article provides a comprehensive overview of RAG, its functioning, and its significance in the world of AI.

?Understanding the Challenges with LLMs

LLMs are powerful tools known for their ability to generate human-like text. However, they exhibit several limitations:

1. Static Nature: LLMs are "frozen in time," meaning they lack real-time information. Updating their extensive training data is not feasible.
2. Lack of Domain-Specific Knowledge: LLMs are trained for general tasks and do not possess knowledge specific to your company's private data.
3. Black Box Functioning: It is challenging to comprehend which sources an LLM considered when arriving at its conclusions.
4. Costly Production: Few organizations have the requisite financial and human resources to produce and deploy foundation models.

These issues negatively impact the accuracy of GenAI applications that leverage LLMs, leading to subpar performance in context-dependent tasks.

?Introducing Retrieval Augmented Generation

Given the limitations of LLMs, there is a need for a more efficient and reliable mechanism. Enter Retrieval Augmented Generation (RAG). RAG fetches up-to-date or context-specific data from an external database and provides it to an LLM during response generation. This reduces the likelihood of hallucinations, resulting in a significant enhancement in the performance and accuracy of GenAI applications.

The Power of RAG in Addressing Recency Issues

One of the primary concerns with LLMs is that they are stuck at a particular time. For instance, the training data "cut-off point" for ChatGPT was September 2021. This means that it lacks updated information about events or developments that happened after this date. As a result, if you ask ChatGPT about something that happened recently, it will not only fail to provide a factual answer but might also concoct a plausible, yet incorrect response.

?RAG addresses this problem by fetching recent or domain-specific data from an external database, which is then made accessible to the LLM at the time of generating a response. This reduces the likelihood of hallucinations and substantially boosts the performance of GenAI applications.

?Domain-Specific Knowledge with RAG

LLMs do not possess knowledge specific to your business, your requirements, or the context in which your application is running. Consequently, they tend to hallucinate when asked domain or company-specific questions. RAG addresses this issue by providing additional context and factual information to your GenAI application's LLM at generation time.

?In addition to addressing the recency and domain-specific data issues, RAG also allows GenAI applications to provide their sources, much like research papers will provide citations for where they obtained an essential piece of data used in their findings.

?Why RAG is a Cost-Effective Solution

There are alternative approaches to boosting the performance of GenAI applications, such as creating your own foundation model, fine-tuning an existing model, or performing prompt engineering. However, RAG is the most cost-effective, easy to implement, and low-risk path to achieving higher performance.

?Deep Dive into Retrieval Augmented Generation

RAG passes additional relevant content from your domain-specific database to an LLM at generation time, alongside the original prompt or question, through a "context window". An LLM's context window is its field of vision at a given moment. RAG is like holding up a cue card containing the critical points for your LLM to see, helping it produce more accurate responses incorporating essential data. To understand RAG, we must first understand semantic search, which attempts to find the true meaning of the user's query and retrieve relevant information instead of simply matching keywords in the user's query. Semantic search aims to deliver results that better fit the user's intent, not just their exact words. Creating a vector database from your domain-specific proprietary data using an embedding model.

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This diagram shows how you make a vector database from your domain-specific, proprietary data. To create your vector database, you convert your data into vectors by running it through an embedding model.

An embedding model is a type of LLM that converts data into vectors: arrays, or groups, of numbers. In the above example, we're converting user manuals containing the ground truth for operating the latest Volvo vehicle, but your data could be text, images, video, or audio.

The most important thing to understand is that a vector represents the meaning of the input text, the same way another human would understand the essence if you spoke the text aloud. We convert our data to vectors so that computers can search for semantically similar items based on the numerical representation of the stored data.Next, you put the vectors into a vector database, like Pinecone. Pinecone's vector database can search billions of items for similar matches in under a second.

Remember that you can create vectors, ingest the vectors into the database, and update the index in real-time, solving the recency problem for the LLMs in your GenAI applications. For example, you can write code that automatically creates vectors for your latest product offering and then upserts them in your index each time you launch a new product. Your company's support chatbot application can then use RAG to retrieve up-to-date information about product availability and data about the current customer it's chatting with.

?Vector databases allow you to query data using natural language, which is ideal for chat interfaces.Now that your vector database contains numerical representations of your target data, you can perform a semantic search. Vector databases shine in semantic search use cases because end users form queries with ambiguous natural language.Semantic search works by converting the user's query into embeddings and using a vector database to search for similar entries.

Retrieval Augmented Generation (RAG) uses semantic search to retrieve relevant and timely context that LLMs use to produce more accurate responses.

You originally converted your proprietary data into embeddings. When the user issues a query or question, you translate their natural language search terms into embeddings.

You send these embeddings to the vector database. The database performs a "nearest neighbor" search, finding the vectors that most closely resemble the user's intent. When the vector database returns the relevant results, your application provides them to the LLM via its context window, prompting it to perform its generative task.

Retrieval Augmented Generation reduces the likelihood of hallucinations by providing domain-specific information through an LLM's context window.

Since the LLM now has access to the most pertinent and grounding facts from your vector database, it can provide an accurate answer for your user. RAG reduces the likelihood of hallucination. Vector databases can support even more advanced search functionality. Semantic search is powerful, but it's possible to go even further. For example, Pinecone's vector database supports hybrid search functionality, a retrieval system that considers the query's semantics and keywords.

?RAG is the most cost-effective, easy to implement, and lowest-risk path to higher performance for GenAI applications. Semantic search and Retrieval Augmented Generation provide more relevant GenAI responses, translating to a superior experience for end-users. Unlike building your foundation model, fine-tuning an existing model, or solely performing prompt engineering, RAG simultaneously addresses recency and context-specific issues cost-effectively and with lower risk than alternative approaches.

Its primary purpose is to provide context-sensitive, detailed answers to questions that require access to private data to answer correctly.

Pinecone enables you to integrate RAG within minutes.

Check out our examples repository on GitHub for runnable examples, such as this RAG Jupyter Notebook.

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