Vector Databases for Enterprise: pgvector vs Dedicated Stores
Vector databases excel at semantic similarity search. They are terrible general-purpose databases. Know the difference …
LLM Cost Optimization: Cut Inference Spend 40-90%
A prototype that costs pennies per request becomes a five-figure production bill without strict token engineering.
Prompt Engineering for Production LLM Applications
Systems that rely on clever phrasing eventually break. Prompt templates must be versioned, tested, and deployed like …
RAG Architecture for Production: Retrieval That Ships
RAG prototypes take an afternoon. Production RAG requires rigorous search engineering and systematic retrieval tuning.
MLOps Pipelines: From Notebook to Production ML
Machine learning models rot in production without the same engineering discipline applied to software.
ML Feature Stores: Fix Training-Serving Skew in Production
Training-serving skew degrades models slowly and silently. Feature stores solve the synchronization problem.
Multimodal AI: Enterprise Document and Audio Pipelines
The enterprise value of multimodal AI is not generating images. It is processing the complex documents and audio your …
LLM Fine-Tuning vs RAG: Choosing the Right Approach
Fine-tuning is expensive, operationally complex, and rarely the right first step for enterprise LLM adoption.
Production AI Features: Prototype to Reliable Scale
Deploy generative models that survive production constraints and deliver actual ROI.