Building Production RAG Systems That Actually Work
Most RAG tutorials stop at "embed, retrieve, generate." Real systems demand hybrid search, re-ranking pipelines, chunk boundary intelligence, and evaluation frameworks that catch failure modes before users do. A practitioner's guide to the architecture that separates prototypes from production.
Continue reading →Attention Is All You Need — Implementing Transformers from First Principles
Walking through the transformer architecture layer by layer, from scaled dot-product attention to multi-head projection, with production considerations at every step.
Event-Driven Architecture at Scale: Patterns That Survive Production
Event sourcing, CQRS, and saga orchestration sound elegant in whitepapers. Here's what actually happens when you operate them at scale — and the patterns worth keeping.
The Agentic Pattern Taxonomy: Tool Use, Planning, and Memory
A structured breakdown of the design patterns emerging in agentic AI — from simple tool-calling loops to multi-agent orchestration with shared memory and planning layers.
Why Your ML Pipeline Needs a Contract Layer
Feature stores solved discovery. Model registries solved versioning. But the handoff between data, training, and serving still breaks silently. A case for schema contracts in ML infrastructure.
Evaluating LLMs Beyond Benchmarks: Building Your Own Eval Framework
MMLU and HumanEval tell you almost nothing about how an LLM will perform in your domain. How to design evaluation pipelines that measure what actually matters for production deployment.
ADRs as Architecture: Decision Records That Actually Drive Design
Most teams write ADRs as post-hoc documentation. The real power is using them as a forcing function for architectural thinking — before the code is written.
Fine-Tuning in Practice: LoRA, Data Curation, and When Not to Fine-Tune
Everyone fine-tunes. Few do it well. The gap between a mediocre adapter and a production-grade one is mostly about data — not hyperparameters.
The Platform Engineer's Manifesto: Infrastructure as Product
Platform engineering isn't DevOps renamed. It's a fundamentally different model — treating infrastructure as an internal product with users, roadmaps, and SLOs.