5 Evals Every Production LLM Needs
Forget MMLU scores. These are the evaluations that actually predict whether your LLM will work in production.
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Insights on AI trust
Practical perspectives on agentic systems, data architecture, and building AI that works in production.
Posts - Page 2
The Real Reason Your RAG App Hallucinates (It's Not Chunking)
Everyone's optimizing chunk size and embedding models. The problem is upstream. Your data pipeline strips context before it ever reaches the vector store.
Read more →Your AI Architecture is Bleeding Money
Cost-per-token is the wrong metric. The real savings come from architectural decisions most teams get wrong.
Read more →The AI Production Readiness Checklist
The comprehensive checklist for launching LLM-powered features. Evaluation, monitoring, fallbacks, cost controls, and incident response.
Read more →Prompt Injection is an Unsolved Problem (Here's How to Mitigate Anyway)
There's no complete solution to prompt injection. Here's the defense-in-depth playbook for production AI systems.
Read more →When to Use Agents vs Deterministic Workflows: A Decision Framework
A concrete decision tree for when to reach for AI agents vs traditional orchestration. Cost, latency, reliability, and compliance dimensions.
Read more →From 11% to 88% GPU Utilization: How We Built 8x Faster LLM Inference
PyTorch leaves 89% of GPU bandwidth on the table. We fixed it with custom Triton kernels. Here's what we learned building Accelerate.
Read more →Eval Debt Will End Careers
Tech debt is slow. Eval debt is sudden. The teams that survive will treat evals like unit tests: written first, run always.
Read more →Agentic AI Is a Cost Center, Not a Strategy
Everyone's racing to deploy AI agents. Most will waste millions. The question isn't 'how do we use more AI?' - it's 'how do we use AI sustainably?'
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