Simplify

Simplicity in AI systems ("Keep It Simple, Stupid") is a strategic advantage for reliability, debugging, and iteration speed, countering the tendency to over-engineer with the latest complex methods. Data quality > Model complexity: a simple model (Logistic Regression, small LLM) on clean data often beats SOTA on dirty data. Prompt engineering vs Fine-tuning: exhaustive prompt optimization is cheaper and easier to maintain than maintaining custom model weights. System architecture: monolithic chains are easier to debug than microservice agents. Occam's Razor: if two models perform similarly, choose the smaller/faster one. Debuggability: simple systems have fewer failure modes; complex RAG pipelines with 10 steps are hard to troubleshoot. Maintenance: simple code is easier for new team members to understand. Reproducibility: complex randomized systems are hard to test. "Complexity tax": every added component (vector DB, cache, reranker) adds latency and failure risk. Start simple, add complexity only when metrics prove it necessary.