AI in Production
Reference architectures, RAG vs fine-tuning decisions, agent reliability, LLMOps, and evals for teams shipping AI to production.
AI Product Architecture in 2026: The Reference Stack for Building Reliable AI Features
What does a production-ready AI product architecture actually look like in 2026? A practical reference stack covering model layer, retrieval, agent orchestration, eval, observability, and the human-in-the-loop tier.
RAG vs Fine-Tuning: The Production Engineer's Decision Framework
RAG vs fine-tuning for LLMs: a practical decision framework covering cost, latency, accuracy, update frequency, and implementation complexity for production AI systems.
Building Reliable AI Agents: Patterns for Failure Recovery, Observability, and Safe Autonomy
AI agents fail in new ways. Learn the reliability engineering patterns—idempotency, circuit breakers, human-in-the-loop, and agent observability—that make autonomous AI systems safe to run in production.
LLMOps: How to Run AI Models in Production Without Flying Blind
MLOps for traditional models is well-understood. LLMOps adds new dimensions: prompt versioning, model routing, drift detection on probabilistic outputs, and cost management. Here's the complete operational framework.
LLM Evals in Production: How to Actually Measure AI Output Quality
Shipping an LLM to production without evals is flying blind. Learn how to build an evaluation framework that measures accuracy, safety, and consistency—and catches regressions before users do.
Agent Engineering: The New Discipline Your 2026 Engineering Team Needs
57% of organizations have agents in production, but 48% skip offline evaluations and 63% skip online monitoring. Agent engineering is the DevOps of 2012—here's the discipline your team needs.
Model Context Protocol (MCP) Explained: The Standard That's Changing How AI Agents Access Tools
MCP (Model Context Protocol) is fast becoming the USB-C of AI tooling. Learn what it is, why it matters, and how to integrate it into your AI agent architecture in 2026.
Unit Economics for AI: Calculating Cost Per Token, Per Inference, and Per Customer
49% of organizations now use unit economics to link cloud consumption to business outcomes. Learn how to calculate cost per token, per inference, and per customer for AI workloads.