Let’s talk about Retrieval-Augmented Generation (RAG). Whether we realize it or not, we all use RAG daily.
If I asked you, “What’s the capital of Zimbabwe?” your thought process would probably go like this:
1. Why do I need to know that?
2. I’ll just Google it.
And if you did, you’d find the answer: Harare—which also happens to be the largest city in Zimbabwe.
This is the beauty of having the world’s information at your fingertips. Instead of memorizing everything, you use your brainpower to process, reason, and make decisions.
AI should work the same way. When using RAG, you’re essentially storing data elsewhere and retrieving that information prior to processing an answer to the question or prompt given.
Why RAG is More Efficient Than Memorization:
Traditional AI models rely on storing vast amounts of knowledge in their parameters. The bigger the model, the more computing power, RAM, and cost required to process information—most of which may never even be used.
If we apply the Pareto principle (80/20 rule) to AI, it’s likely that for most use cases, a model only uses 20% of its training data to handle 80% of real-world tasks. So why force it to memorize everything when it can just retrieve knowledge on demand?
Instead of training a massive model that tries to “know everything,” RAG keeps models smaller, cheaper, and more adaptable.
Applying RAG to Sales AI:
Since I typically write from a sales perspective, imagine a model trained specifically to be great at selling.
Now, let’s say we want this AI to sell cars. Instead of fine-tuning the model with every single piece of knowledge about every car ever made, we just:
• Train it to be a sales expert (negotiation tactics, objection handling, deal closing).
• Use RAG to pull in car-specific data (pricing, specs, competitive advantages, ideal customer profile, etc.) only when needed.
This approach is faster, more cost-effective, and scalable compared to retraining an entire model every time new information becomes available.
Takeway:
AI models should work like smart humans—focusing on expertise and retrieving information when necessary, rather than memorizing everything.
That’s why RAG isn’t just an optimization—it’s a fundamental shift in how we think about AI efficiency.
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