Resources
Written by the BigAI engineers running the projects — with numbers, with trade-offs, not rewritten vendor documentation.
Three architectures solving three different problems. Choosing wrong does not fail the project immediately — it raises cost and complexity until a rebuild becomes unavoidable.
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The two approaches are often framed as alternatives. RAG solves a knowledge problem; fine-tuning solves a behaviour problem. Confusing them is a common way to overspend.
Most Spark spend is wasted in places that are easy to fix: the wrong storage format, partitions that are too small, avoidable shuffles, and clusters idling overnight.
Change data capture gives you near real-time synchronisation without running heavy queries against the transactional system. It also brings its own traps.
A claim of "92% accuracy" means almost nothing unless you state which question set it was measured on and what counted as correct.
Models rarely fail abruptly. They decay — and without monitoring, the first person to notice is usually a customer.
Resources
42 self-assessment questions across four levels, with a scoring sheet. 18 pages.
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