- ai
- machinelearning
- datascience
- programming
- rag
•
•
•
•
-
The Copy-Paste Trap: When Knowing “Everything” Still Leaves You with Nothing
AI-assisted coding gives you a warm, dopamine-rich feedback loop. But somewhere along the way, we stop exercising deep engineering knowledge. Here is how to rebuild understanding.
-
Why “Just Prompting” Fails on Private Data: A RAG Post-Mortem
Naïve RAG breaks in production in surprising ways. This article walks through real failure modes, like the 'Lost in the Middle' problem, and the guardrails needed to make RAG production-ready.
-
XGBoost: When Gradient Boosting Meets Regularization
A deep technical breakdown of why XGBoost is gradient boosting on steroids, minimizing a regularized objective function to balance prediction errors and model complexity.
-
PCA (Principal Component Analysis): Finding the Hidden Structure in High-Dimensional Data
Step-by-step breakdown of Principal Component Analysis, computing covariance matrices, eigenvalues, and how to capture maximum variance in lower-dimensional spaces.
-
I Built a RAG Pipeline. Then I Realized Retrieval Is the Real Model
Most RAG failures come from retrieval quality, not the language model. Here's what changed when I stopped optimizing prompts and started optimizing retrieval.