Understanding Context window and Retrieval-Augmented Generation (RAG) in Large Language Models

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LLM: Context Window and RAG

In just two years, we have seen the impressive rise of Large Language Models (LLMs) on a massive scale, with releases like ChatGPT. These models have shown incredible capabilities, but they also have a limitation with the context window. If you have ever used an LLM and tried to input a large amount of information, you have likely encountered the “Context Window Mark” issue.

Before we understand more about the context window, lets first quickly understand what tokens are.

Understanding Tokens

Tokens, in the context of language models, are the basic units of text processing. They represent individual words, punctuation marks, or other linguistic elements within a given piece of text.

Tokenization works YourGPT

We have added the sentence: “YourGPT Chatbot is a great tool to automate your customer service with AI. With the No-Code Builder Interface, quickly create and deploy your AI chatbot.” where each word and the punctuation mark are separate tokens, adding up to 35 tokens in total.

Understanding tokens is important because each token consumes a portion of the model’s memory limit, as defined by the context window. This constraint directly impacts how much information the model can process at once. Now that we know about tokens, let’s see the concept of the context window and its impact on LLMs, along with the concept of Retrieval-Augmented Generation (RAG) and the influence of a long context window.


What is a context window?

what is context window

The context window in language models refers to the maximum length of text (measured in tokens) that a model can consider at one time for processing. This limitation affects how much information the model can analyse and respond to in tasks such as translation, answering questions, or generating text.

Context window sizes differ across LLMs; for example, GPT-3.5-turbo-1106 has a context window of 4,000 tokens. Gemini 1.5, on the other hand, expands this to 1 million tokens.

This means that the combined count of input tokens, output tokens and other control tokens cannot exceed 4,000 in the case of GPT-3.5-turbo and 1 million for Gemini. In simple terms, it imposes a restriction on the amount of instruction you can provide to the system and the maximum tokens allowed for response generation. If this limit is exceeded, an error occurs.

The problem with the context window in large language models is its fixed size, which restricts the amount of text the model can consider at one time. This can make it hard for the model to understand and answer questions that require more context-specific information.

To Fix this Context window issue, the researchers have introduced an approach Called RAG

What is RAG?

what is RAG?

RAG stands for Retrieval-Augmented Generation. RAG is a hybrid approach to natural language processing that enhances the capabilities of large language models by combining the generative powers of models like GPT, Claude, and Gemini with their information retrieval functionalities.

RAG works by retrieving the relevant documents or data from a large corpus and then using this context information to generate responses to user queries. This method allows the model to produce more accurate, informed, and contextually relevant outputs, especially in cases where the answer needs specific knowledge that is not stored in the model’s training data

RAG and Long Context

There is a debate in the AI community about long context v/s RAG:

  • Enhanced Information Retrieval: Long Context LLMs can process vast amounts of information within their extended context windows, reducing the need for external data retrieval via RAG. This capability addresses one of the primary motivations for RAG—augmenting LLM knowledge by fetching relevant information from external sources.
  • Flexibility and Adaptability: Long Context LLMs integrate retrieval and reasoning throughout the decoding process, allowing for more nuanced and adaptable responses. On the other hand, RAG retrieves information upfront, which may limit its flexibility in dynamically evolving conversations or complex reasoning tasks.
  • Scalability and Data Complexity: RAG’s architecture enables it to scale to trillions of tokens, surpassing the current capabilities of long-context LLMs. This makes RAG essential for scenarios involving vast datasets or complex, structured data that changes over time, such as code repositories or dynamic web content.
  • Collaboration and Complementary Strengths: RAG and Long Context LLMs can complement each other rather than being mutually exclusive. RAG’s precision in retrieval can enhance long-context LLMs’ broad reasoning capabilities. This collaboration mirrors the cooperative relationship between different types of memory storage and processing in computer architecture.
  • Cost Considerations: Long contexts can be expensive due to the computational resources required. RAG, on the other hand, offers cost advantages. The cost-effectiveness of RAG makes it a preferred choice for cost-sensitive applications.

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Conclusion

The combination of context windows and Retrieval-Augmented Generation (RAG) represents a significant advancement in improving the efficiency of Large Language Models (LLMs). Context windows determine how much information LLMs can handle at once, sometimes limiting their potential. RAG addresses this by incorporating external data, enhancing response accuracy and context relevance.

The AI community continues to discuss long-context models versus RAG. Instead of choosing one over the other, integrating RAG with long-context LLMs is the ideal solution, creating a powerful system capable of efficiently retrieving and processing large-scale information.

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