Android Studio updates so slowly that by the time it finishes installing, Google has already released 3 new Android versions and renamed the IDE twice.
Anthropic pays $750,000+ a year for engineers who know how to build LLMs from scratch.
Stanford just released the exact lecture that teaches it - 1 hour 44 minutes, free, straight from CS229.
Bookmark and watch it this weekend.
It'll teach you more about how ChatGPT & Claude actually work than most people at top AI companies learn in their entire careers.
RAG vs. CAG, clearly explained!
RAG is great, but it has a major problem:
Every query hits the vector DB. Even for static information that hasn't changed in months.
This is expensive, slow, and unnecessary.
Cache-Augmented Generation (CAG) addresses this issue by enabling the model to "remember" static information directly in its key-value (KV) memory.
In fact, you can combine RAG and CAG for the best of both worlds.
Here's how it works:
RAG + CAG splits your knowledge into two layers:
↳ Static data (policies, documentation) gets cached once in the model's KV memory
↳ Dynamic data (recent updates, live documents) gets fetched via retrieval
This gives faster inference, lower costs, and less redundancy.
The trick is being selective about what you cache.
Only cache static, high-value knowledge that rarely changes. If you cache everything, you'll hit context limits. Separating "cold" (cacheable) and "hot" (retrievable) data keeps this system reliable.
You can start today. OpenAI and Anthropic already support prompt caching in their APIs.
I have shared my recent article on prompt caching below if you want to dive deeper.
Have you tried CAG in production yet?
Below, I have quoted an article that I wrote on prompt cashing and how Claude Code achieves a 92% cache hit-rate. Give it a read.
Atlassian's CEO after firing the engineer who built their $1.79B infrastructure and the guy released a 38-minute breakdown of everything he built, free for anyone to copy