Good morning everyone!
Today, we’re diving into “the death of Retrieval-Augmented Generation (RAG).” With advancements in large language models (LLMs), many clients have asked, “Why use RAG if models like Gemini can process millions of tokens?” Let's investigate.
The Evolution of LLM Compression Methods
LLMs offer incredible possibilities but can be expensive and complex to deploy. Researchers are finding ways to compress these models without sacrificing performance, leading to significant breakthroughs.
RAG vs. Long Context Length
About RAG
RAG retrieves and adds relevant information to an LLM prompt from external sources like PDFs or databases. It's useful for private data or topics not seen during the LLM's training.
When is RAG Good?
RAG is ideal for handling large collections of documents that can't fit within a single LLM context window. It allows for quick updates without retraining the model, making it perfect for integrating documentation and code snippets that change frequently.
Contrary to popular belief, RAG systems are fast and accurate due to efficient document indexing. By selectively including relevant information, RAG reduces noise and potential hallucinations.
About Long Context Length
Long context models, like Gemini-1.5-pro, can process up to 2 million tokens, allowing them to reason over extended text passages. This is great for tasks requiring the examination of large amounts of data simultaneously.
When is Long Context Length Good?
Long context is ideal when processing large datasets and when a long processing time is acceptable. It's perfect for understanding an entire new codebase, as the model can see every file and line of code.
Evaluations of Long Context Length
New LLMs use the "needle in a haystack" evaluation to test their ability to retrieve specific information from long texts. While useful, it doesn’t fully assess how well the model uses the information.
Is RAG Going Away?
No. While longer context lengths reduce the need for RAG, it remains advantageous in many scenarios:
Large datasets
Time-critical processing
Cost-effective applications
RAG excels in customer support systems and real-time data integration, while long context models are best for complex single-document analysis and summarization tasks.
Summary: RAG vs. Long Context Length
| Characteristic | RAG | Long Context Length |
| Speed | Fast | Slower |
| Cost | Lower | Higher |
| Accuracy | High | Variable |
| Use Case | Large datasets, real-time updates | Single-document analysis, summarization |