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12 articlesModel Migration as Database Migration: Safely Switching LLM Providers Without Breaking Production
Switching LLM providers or upgrading model versions is more like a database schema migration than a config change. Here's the production playbook engineers actually need.
LLM-Powered Data Migrations: What Actually Works at Scale
A practitioner's guide to using LLMs for schema migrations and ETL automation — covering the silent failure modes, layered validation architecture, schema-based prompting, and when LLMs should not replace traditional pipelines.
LLMs as Data Engineers: The Silent Failures in AI-Driven ETL
LLMs handle messy data edge cases that hand-coded ETL pipelines miss — but they also produce confidently wrong transformations with no error signal. Here's the validation, sandboxing, and monitoring stack that makes AI-augmented ETL safe in production.
What Model Cards Don't Tell You: The Production Gap Between Published Benchmarks and Real Workloads
Model card benchmarks are measured under ideal conditions that rarely match production. Here's the gap every team discovers too late — and the internal benchmark suite that catches it before deployment.
Model Deprecation Is a Systems Migration: How to Survive Provider Model Retirements
When your inference provider sunsets a model, swapping the model ID is the least of your problems. Here's the engineering discipline that keeps production AI running through retirements.
The Model Portability Tax: How to Architect AI Systems You Can Actually Migrate
Every model swap is a partial rewrite if you didn't design for portability. Here's the abstraction layer, capability negotiation, and regression testing infrastructure that turns model migrations from crisis deployments into planned operations.
Model Upgrade as a Breaking Change: What Your Deployment Pipeline Is Missing
Foundation model updates silently break downstream systems through output format shifts, tone changes, and reasoning divergence. Here's the infrastructure to detect and manage it.
Multi-User AI Sessions: The Context Ownership Problem Nobody Designs For
When multiple users share an AI assistant, context becomes a shared mutable resource with no access control. Here's how context leaks, personalization bleeds, and race conditions appear at team scale — and the isolation patterns that actually prevent them.
The Multilingual Quality Cliff: Why Your LLM Works Great in English and Quietly Fails Everyone Else
English-first LLMs degrade silently for non-English users. Here's the 20–40% accuracy gap, why standard eval suites miss it, and the per-language benchmarking and routing strategies that surface the gap before your users do.
The Multilingual Token Tax: What Building AI for Non-English Users Actually Costs
Tokenization is 3–8× worse for CJK, Arabic, and Hindi scripts — a hidden cost multiplier that changes every API budget, latency model, and eval strategy built around English benchmarks.
Organizational Antibodies: Why AI Projects Die After the Pilot
70-90% of AI projects never escape proof-of-concept. The technology works — the organization doesn't. Here's how engineers and technical leaders navigate the resistance patterns that kill AI initiatives after a successful pilot.
The ORM Impedance Mismatch for AI Agents: Why Your Data Layer Is the Real Bottleneck
ORMs and REST APIs were designed for human interaction patterns — single-entity reads, lazy loading, and session-scoped transactions. AI agents do none of these things. Here's why your data layer is silently killing agent performance and what to do about it.
About Tian Pan
I'm Tian Pan, an engineer-founder focused on agentic engineering — building autonomous AI systems and scaling engineering teams. I write practical guides on system design, technical leadership, and shipping with AI agents. Previously an early engineer at Uber, Brex, and IoTeX.
