X @@akshay_pachaar · May 19, 2026
Full analysis by SuperBM
Akshay 🚀: RAG vs. CAG, clearly explained!
4/10 Mixed
Explains Cache-Augmented Generation vs RAG for faster, cheaper LLM inference.
Key Insights
- Separating static and dynamic knowledge is a practical architectural pattern.
- Prompt caching is a real feature, not a novel generation method.
- The post uses marketing language to rebrand existing techniques.
Caveats & Flags
- Conflates prompt caching with a new paradigm 'CAG'—just rebranding.
- Claims CAG solves RAG slowness but caching also adds latency and cost.
- Unsupported claim that Claude Code achieves 92% cache hit-rate.
Valid Points
- Prompt caching can reduce repeated retrievals for static data.
- Combining retrieval and caching may optimize latency for stable vs. volatile data.
- OpenAI and Anthropic do offer prompt caching in their APIs.
Counterpoints
- Every query still processes the cached KV memory, which can be large.
- Vector DB retrieval is often sub‑millisecond, caching adds engineering complexity.
- Cache hit-rate varies heavily by workload; 92% is not a general benchmark.