Using RAG Workflow with Generative AI vs. Using Long Context

In the evolving landscape of artificial intelligence, two prominent methods for handling extensive information are Retrieval Augmented Generation (RAG) and Long-Context (LC) Large Language Models (LLMs). This article explores the differences between these approaches, highlighting their strengths and weaknesses, and introduces a hybrid method that combines the best of both worlds.

Understanding RAG and LC

Retrieval Augmented Generation (RAG)

RAG is a technique where the AI retrieves relevant information from a large dataset and then generates responses based on this retrieved data. Imagine you have a vast library, and instead of reading every book, you ask a librarian (the AI) to fetch the most relevant books for your query. The AI then uses the information from these books to answer your question.

Example: Consider an HR chatbot designed to answer questions about employee benefits. If an employee asks about the reimbursement of outpatient department (OPD) expenses under the group insurance policy, the RAG-based system will first retrieve the specific document or section related to OPD reimbursement. It will then generate a response based on this retrieved information, ensuring the answer is precise and relevant.

Long-Context (LC) Large Language Models

LC LLMs, on the other hand, are designed to process and understand long pieces of text directly. These models can handle extensive contexts without needing to fetch additional information, making them ideal for tasks requiring deep understanding and continuity.

Example: Using the same HR chatbot scenario, if an employee asks about the reimbursement of OPD expenses, the LC-based system will incorporate the entire group insurance policy into the AI prompt. This allows the model to provide a comprehensive answer by understanding the full context of the policy, even if the question involves multiple aspects of the insurance coverage.

Benchmarking Results

Recent research compared RAG and LC across various datasets using models like Gemini-1.5-Pro and GPT-4. The findings revealed that LC models generally outperform RAG in terms of accuracy and depth of understanding when sufficient computational resources are available. However, RAG remains advantageous due to its lower computational cost.

Performance:

• The Gemini-1.5-Pro model achieves the highest performance with the LC method (49.70), followed by GPT-4O with Self-Route (48.89).
• The RAG method consistently shows lower performance across all models, with scores like 32.60 for GPT-4O and 37.33 for Gemini-1.5-Pro.

Cost:

• The LC method incurs the highest cost (100%) across all models.
• The RAG method is the most cost-effective (17%) for all models.
• The Self-Route method has a moderate cost, with GPT-3.5-Turbo being the most cost-effective at 39%.

The Self-Route Method

To bridge the gap between performance and cost, researchers proposed the Self-Route method. This hybrid approach dynamically routes queries to either RAG or LC based on the complexity of the query and the model's self-assessment.

The Self-Route method achieves a balanced performance, with GPT-4O scoring 48.89 and Gemini-1.5-Pro scoring 46.41 while maintaining a moderate cost (e.g., 61% for GPT-4O).

Conclusion

In summary, while LC LLMs offer superior performance for tasks involving long contexts, RAG remains a valuable approach due to its cost efficiency. The innovative Self-Route method combines the strengths of both, providing a balanced solution that optimizes performance and cost. As AI continues to evolve, such hybrid approaches will likely become more prevalent, offering versatile and efficient solutions for a wide range of applications.

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