RAG vs CAG: Retrieval vs Cache Augmented Generation Explained

Bashiri Smith · Facebook reel · 2026-08-29 · 1:31 · 61,273 views · Open on Facebook

Topics: Retrieval-Augmented Generation (RAG), Embeddings & Vector Databases, AI System Design & Architecture · Level: beginner

Summary

The video compares two ways to ground an LLM in your own documents. Retrieval-Augmented Generation (RAG) chunks and embeds documents into a vector database, then retrieves similar chunks for each query. Cache Augmented Generation (CAG) preloads all the documents into the model's context window as a KV cache, so no retrieval step is needed. The creator stresses that choosing between RAG, CAG and other retrieval architectures depends on context window limits, scalability, cost, latency, accuracy and data freshness.

Key points

• RAG pipeline: chunk the documents → turn each chunk into an embedding (a list of numbers that represents meaning) → store the embeddings in a vector database, where chunks with similar meanings sit closer together.
• At query time in RAG, the user's question is embedded, a similarity search finds the closest chunks, and the LLM writes its answer from that retrieved context.
• RAG is used when there are too many documents (often thousands) to fit into an LLM's context.
• CAG (Cache Augmented Generation) skips chunking and retrieval. All documents are preloaded into the model's context window, and the model builds a KV cache from them.
• In CAG, each user query is added to the already-loaded context, and the LLM answers from the cached documents.
• CAG's main limitation is the size of the model's context window.
• Weigh scalability, cost, latency, accuracy and data freshness when choosing RAG, CAG or another retrieval architecture.
• Building a RAG demo is one thing. Knowing when to use RAG versus CAG is what moves you toward production-level AI engineering.

Resources mentioned

• BASWE.Ai Engineer (Skool community) · community · skool.com · paid
  The creator's paid community and program, with an AI learning roadmap (including the full ops and evaluation track), daily calls with engineers and recruiters, resume and portfolio help, and a job-search pipeline.
  Also in: Basic RAG Pipeline in 60 Seconds: From Documents to Grounded Answers (Bashiri Smith on Facebook · notes), Pointer to Bashiri Smith's Complete AI Engineer Roadmap for 2026 (Bashiri Smith on Facebook · notes), Step-by-Step Roadmap to a $200K+ AI Engineering Role (Bashiri Smith on Facebook · notes), How to Evaluate a RAG Pipeline: Retrieval vs. Generation (Interview Answer) (Bashiri Smith on Facebook · notes) and 76 more

Try this

• Learn both RAG and CAG, and when to use each, instead of only knowing how to build a RAG demo.
• Before choosing an architecture, check context window limits, scalability, cost, latency, accuracy and data freshness.
• Optional (creator's call to action): comment 'CAG' on the reel to get the community link, or join the BASWE.Ai Engineer Skool community.