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 →Building Effective LLM Evaluation Frameworks for Production
Benchmarks do not measure your product. A three-layer evaluation framework, LLM-as-judge calibration, synthetic coverage, and continuous monitoring for real traffic.
ArticleBuilding Agentic Systems for Production: Patterns That Work
Narrow scope, planning as a separate concern, type-disciplined tool use, memory as a product decision, bounded autonomy, and observability built for non-determinism.
ArticleDesigning Scalable RAG Systems: Patterns and Pitfalls
Chunking as a modeling decision, hybrid retrieval, cross-encoder re-ranking, and the honest evaluation discipline that separates RAG prototypes from production systems.
ArticleBuilding Production AI Systems: From Prototype to Scale
Latency as a design constraint, cost as a first-order concern, reliability in a probabilistic world, and the observability that makes all of it debuggable.
ArticleDistributed Training at Scale: Data Parallelism to Pipeline Parallelism
Data, tensor, and pipeline parallelism, ZeRO optimizer sharding, and the 3D recipe that frontier training runs actually use on real hardware.
ArticleModern MLOps: Building Resilient Data and Training Pipelines
The four pipelines, data validation as a first-class step, reproducible lineage, risk-matched deployment strategies, and the monitoring layer that keeps models useful after launch.
ArticleEvent-Driven Architecture for Machine Learning Systems
Events as the ML source of truth, streaming feature computation, continual learning via replay, and streaming inference for feedback loops batch systems cannot match.
BlogAttention 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.
ArticleUnderstanding Transformer Architectures: From Attention to Production
From the foundational attention mechanism to KV caching, flash attention, continuous batching, and the production optimizations that make serving transformers tractable.
BlogEvent-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.