RAG vs Fine-Tuning: When to Use Which for Your AI Product
Two popular approaches to making LLMs smarter about your domain — but they solve very different problems. Here's our decision framework.
Engineering deep-dives, AI strategy guides and business insights from the team that ships them.
Everything here comes out of client work. We write a piece when a question has come up on three separate projects and we are tired of explaining it from scratch.
Architecture decisions with the trade-offs left in: when retrieval beats fine-tuning and when it does not, how multi-tenant data isolation actually gets enforced, and what an AI build really costs once you include evaluation and monitoring. No vendor pitches, no predictions about the next five years.
Founders and engineering leads deciding whether to build something, and the engineers who will maintain it afterwards. If a piece raises a question about your own system, ask us directly — we answer those for free.
New pieces go out roughly monthly. The full archive is below.
Two popular approaches to making LLMs smarter about your domain — but they solve very different problems. Here's our decision framework.
Speed and quality seem like opposites. We've spent 3 years engineering a process that delivers both.
UAE's regulatory environment, talent pool, and government investment are creating ideal conditions for AI companies.
Shared database vs. separate databases, row-level security, and how to handle white-labeling without losing your mind.
We break down what AI projects actually cost — compute, APIs, engineering time, and iteration budget.
We've built dozens of backends in both. Here's where each shines — and why for AI-heavy workloads, there's usually a clear winner.
We write architecture reviews for teams weighing up an AI build. Ask for one.