RAG Architecture Decision Guide: When to Use What
A PM-friendly guide to RAG architecture decisions. Covers the decision tree, chunking strategies, embedding models, retrieval approaches, and the failure modes that kill most RAG systems.
RAG Architecture Patterns: When to Use What
Most RAG implementations start the same way: chunk your documents, embed them, stuff the top-K results into the prompt. That works in a demo. In production it breaks in predictable ways. Here is how to think about which RAG pattern fits your problem.
The Architecture of Production ML Pipelines
Most ML tutorials end at model training. Production ML starts where those tutorials stop. Feature stores, model registries, serving infrastructure, monitoring, and feedback loops — the full stack that makes a model usable in a real product.
RAG vs. Fine-Tuning vs. Prompt Engineering: A Life Sciences Product Manager's Decision Framework
Three techniques. One wrong choice can cost you six months and $500K. Here is the framework I use to pick the right approach for every life sciences AI use case.
Complexity is the Enemy of AI Adoption
The AI adoption problem is not a model problem. It's a UX problem. Sophisticated models that are hard to use don't get used. Simpler models that fit naturally into workflows do. The metric that matters is adoption, not accuracy.
The Build vs Buy Decision for Enterprise LLM Integration
Build vs buy for LLMs is not a binary choice. It's a spectrum from pure API calls to fully custom models, and the right answer depends on your data sensitivity, accuracy requirements, cost at scale, and team capabilities. Here's the decision matrix I use.
Fine-Tuning vs RAG vs Prompt Engineering: A Decision Guide
The three main approaches to customizing AI behavior are not interchangeable. Each solves a different problem. Choosing the wrong one wastes months of engineering and budget.
Latency, Accuracy, Cost: Pick Two
Every AI system is a set of tradeoffs between latency, accuracy, and cost. You can optimize for any two, but not all three simultaneously. The organizations that build good AI systems are the ones that choose their constraints deliberately, not by accident.
How Embedding Models Actually Work
Embedding models convert text into vectors that capture meaning. They're the foundation of RAG, semantic search, and recommendations — but most teams use them without understanding why they work or where they break.