Blog
Practitioner-written guides.
Deep dives on RAG systems, LLM evaluation, prompt safety, and AI engineering careers — written by engineers who grade production work, not generated content.
- 8 min
Fundamentals · 2026-05-06
What Is Production AI Engineering? (And Why It's Different From ML Research)
Production AI engineering means shipping LLM systems that work at scale under adversarial conditions. Here is what separates it from research and why the skills gap is real.
- 12 min
Fundamentals · 2026-05-06
RAG System Architecture: A Production Engineer's Guide
Retrieval-augmented generation has a simple concept and a complex production reality. This guide covers the full architecture: ingestion, retrieval, augmentation, and generation — with the tradeoffs that matter.
- 10 min
Fundamentals · 2026-05-06
LLM Evaluation: Why Offline Evals Must Come Before Online Metrics
Online metrics (latency, engagement, CSAT) tell you what happened. Offline evals tell you what will happen. Building your eval harness before you ship is not optional — it's the difference between iteration and guessing.
- 11 min
Fundamentals · 2026-05-06
Chunking Strategies for RAG: Fixed, Semantic, Hierarchical, and When to Use Each
How you chunk documents is the single highest-leverage decision in a RAG system. This guide covers every major strategy, their tradeoffs, and how to decide without running 40 experiments.
- 12 min
Fundamentals · 2026-05-06
Vector Databases Compared: Pinecone, Weaviate, Qdrant, pgvector (2026)
Choosing a vector database is a deployment decision, not a capability decision. Here is an honest comparison of the major options — including the cases where Postgres with pgvector is the right answer.
- 10 min
Fundamentals · 2026-05-06
Embedding Models Compared: OpenAI, Cohere, BGE, E5, and How to Choose (2026)
The embedding model you choose determines your retrieval ceiling. A bad embedding model cannot be fixed with a better vector database. Here is how to evaluate and choose.
- 9 min
Fundamentals · 2026-05-06
Retrieval Recall vs. Precision: How to Measure RAG Quality Without Fooling Yourself
Recall@k is the metric that matters most in RAG retrieval — but measuring it correctly requires a held-out evaluation set most teams don't have. Here is how to build one and use it.
- 8 min
Fundamentals · 2026-05-06
Prompt Engineering Is Not Engineering: The Case for Structured Evals
Most prompt engineering is intuition dressed up as process. Structured evals — offline test suites that measure prompt changes against a golden set — are what separates engineering from guessing.