A VRAM budget formula for LLM serving, and three optimization strategies mapped to the traffic patterns that trigger the OOM.
Mostafa Ibrahim
Three conditions that must hold before splitting prefill from decode pays off, and why chunked prefill is the right default below that threshold.
Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch
Large Language ModelsFive failure modes that survive constrained decoding, and why your schema validator will never catch them.
The five MLOps monitoring assumptions agents break, and which inherited signals now pass failed runs as healthy.
Standard prompt attacks are merely the beginning. A structured framework to map and mitigate the backend attack vectors of agentic workflows.
Inference Scaling (Test-Time Compute): Why Reasoning Models Raise Your Compute Bill
Large Language ModelsWhy reasoning models dramatically increase token usage, latency, and infrastructure costs in production systems
Agentic RAG Failure Modes: Retrieval Thrash, Tool Storms, and Context Bloat (and How to Spot Them Early)
Large Language ModelsWhy agentic RAG systems fail silently in production and how to detect them before your cloud bill does
A practical guide to choosing between single-pass pipelines and adaptive retrieval loops based on your use case's complexity, cost, and reliability requirements
Explore your data easily with these 6 1-lines of code using Pandas
The Worst 4 Mistakes I Made in My First Research Project at University College London
Artificial IntelligenceLearn from my mistakes and excel at your own research project