RAGonomics: Optimizing Retrieval Augmented Generation Economics and Performance

Retrieval Augmented Generation (RAG) has emerged as a promising approach to combine the strengths of large language models (LLMs) with semantic retrieval techniques. This hybrid approach offers improved performance in various NLP tasks, such as question answering, summarization, and chatbots. However, the economics of RAG use cases have been challenging, especially with the high cost of using GPT4 for input and output tokens. Fortunately, recent advancements in open-source models and cloud LLM providers offer more cost-effective and efficient solutions. In this blog post, we will explore the key factors that contribute to the economics and performance of RAG systems.

Open-Source Models: A Game-Changer for RAG Economics

Until recently, GPT4 was the go-to choice for RAG use cases, but its high cost made it less accessible for many developers and organizations. Today, open-source models like LLama 3 8b, Mistral 7b, and Microsoft Phi3 2.6b have become good enough for most RAG applications. These models offer a more affordable alternative while maintaining a high level of performance.

Cloud LLM Providers: Unlocking the Potential of Open-Source Models

The emergence of cloud LLM providers, such as Fireworks.ai, has further enhanced the economics of RAG systems. These providers offer excellent pricing on open-source models and allow users to host them in their own datacenters at a fraction of the cost of proprietary solutions. This not only reduces the overall expenses but also enables users to benefit from frequent model updates and improvements.

Focus on Semantic Retrieval for Optimal Performance

To optimize the performance of RAG systems, it is crucial to focus on semantic retrieval (or hybrid semantic/lexical) techniques. By optimizing for recall and precision, users can employ the minimum number of high-quality chunks, which in turn enhances the reasoning capabilities of smaller LLMs. This approach not only improves the efficiency of the system but also reduces the need for the largest models for basic RAG question/answer use cases.

Invest in Chunk Retrieval Mechanisms for Better RAG Performance

Finally, the key to achieving excellent RAG performance is to invest in refining your chunk retrieval mechanisms. By focusing on improving this aspect, users can significantly enhance the overall performance of their RAG systems without relying on the largest models. This will enable them to develop more efficient and cost-effective solutions for various NLP applications.

In conclusion, RAGonomics is all about optimizing the economics and performance of retrieval augmented generation systems. By leveraging open-source models, cloud LLM providers, and focusing on semantic retrieval techniques, users can create more efficient and cost-effective RAG systems that deliver exceptional performance. As the field continues to evolve, we can expect further advancements in this space, enabling even more affordable and powerful RAG solutions for a wide range of applications.

Title: RAGonomics: Optimizing Retrieval Augmented Generation Economics and Performance Author: [Your Name] Date: [Today’s Date]

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Facts Used:

• • The economics of RAG (retrieval augmented generation) use cases, as early as a year ago was not great. Everyone was using GPT4 which was very expensive for input and output tokens.
• • Open source models like LLama 3 8b and Mistral 7b and Microsoft Phi3 2.6b are now good enough for most RAG use cases
• • Cloud LLM (large language model) providers like Fireworks.ai have excellent pricing on all open source models. They can also be cheaply hosted in your own datacenter.
• • Focusing on the Semantic retrieval (or Hybrid Semantic/Lexical) and optimizing for recall and precision allows you to use the minimum number of high quality chunks, which optimizes the reasoning capabilities of smaller LLMs.
• • Spend the budget on fixing your chunk retrieval mechanisms and you won’t need the largest models for basic RAG question/answer use cases.