Lately, I ve been looking closely at how independent builders and small teams are managing AI knowledge bases. It feels like the default "industry standard" is to immediately reach for a complex RAG pipeline and a heavy, paid Vector Database.
But I'm starting to wonder if we are over-engineering this for 90% of standard use cases.
Vector DBs are incredibly powerful for massive scale, but for smaller or non-massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare.
1. There is no such thing as too much social proof 2. The higher price, the better customers 3. Empower users, not market them 4. Talking to customers is a shortcut to success 5. Having an email list is a superpower 6. Nobody wants to book a meeting with you 7. Showing the product > Describing the product 8. Nail one acquisition channel before adding the second 9. Sell positive future, not product features 10. Your customers are the best promoters 11. Distribution is more important than the content 12. Paying customers is the true validation You can see how we implement all these rules on the landing page of MakerBox Marketing Workbook https://www.makerbox.club/workbook
•