🔧 You should use CAG instead RAG everywhere

The most hyped buzzword (RAG)

The technology known as Retrieval-Augmented Generation (RAG) exists for contemporary use, RAG serves parties who need to appear knowledgeable by delivering search engine results for spontaneous conversations.
Basically, language models get assistance from RAG to obtain information instantaneously to enhance their responses. Cool, right?

Multiple things about Retrieval-Augmented Generation RAG may surprise you, even though it seems impressive initially, RAG behaves as a demanding diva through excessive fetch time delays and random incorrect information retrieval, which leads the system to become tangled, similar to knotted earbuds post-workout 😅

Most of the cases where you want to throw punches over your mattress are (a.k.a common errors):

• Retrieval Latency
• Retrieval Errors
• System Complexity

So, enter Cache-Augmented Generation (CAG):

The intellectual community has introduced a fresh method known as Cache-Augmented Generation (CAG).
CAG functions similarly to a prepared friend who always arrives equipped by loading every piece of vital information directly into an expanded memory database belonging to language models, which functions similarly to an oversized sticky note while saving settings. The model uses CAG to access information with speed without needing to rush during performance because it has all the needed content readily available. CAG utilizes preloaded data in the model's extended memory system to provide instant responses as well as smooth setup processes similar to your preferred music playlist.
Below is an image, just in case you may want to see some diagrams with scientific jargon and floating letters:

• Speed Demon: The model no longer requires delays to retrieve information. The system provides all necessary information in advance, resulting in rapid responses

• The real-time search removal from this system reduces the number of mistakes made during information retrieval and accuracy for the win!

The system operates optimally because complex retrieval methods are unnecessary

There are fewer moving parts, which means less drama

Tech wizards used benchmarks testing CAG to discover that some long-context LLMs provided superior performance over regular RAG systems. CAG demonstrates excellent performance when working with compact knowledge bases since it delivers optimal results while limiting unnecessary complexity

For certain gigs, especially where the info pool isn't a bottomless pit, CAG offers a slick and efficient alternative to RAG
✨ It keeps things lean, mean, and running like a dream ✨

Limitations

Nevertheless, nothing is just a sunny day in the summer, we have some limitations like Limited Knowledge Size as CAG requires the entire knowledge source to fit within the context window, making it less suitable for tasks involving extremely large datasets and Context Length Constraints as the performance of LLMs may degrade with very long contexts