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12 articlesYour Eval Harness Is a Museum: How Production Failures Should Write Tomorrow's Tests
Static eval harnesses grow stale as your product grows — they only test what the author anticipated. A production-driven feedback loop automatically converts real failures into permanent regression tests, keeping your eval suite aligned with actual user behavior.
Ship Your AI Feature Before It Feels Ready
Why the first 500 real users generate more actionable signal than four more weeks of prompt tuning — and how to design an early access program that captures it without burning trust.
The SLA Illusion: Why 99.9% Uptime Means Nothing for AI-Powered Features
Traditional uptime SLAs guarantee the endpoint responds — not that it responds well. Here's why AI-powered features need a different reliability contract.
Soft Constraints vs. Hard Constraints in LLM Systems: Why the Mismatch Causes Real Failures
Treating system prompts as security controls is an architectural mistake that causes breaches. A practical breakdown of constraint layers in production LLM systems and how to match enforcement strength to actual risk.
The First Token Lies: Why Context Loading—Not Inference—Controls Your AI Feature's Latency
Your AI feature's perceived speed is determined before the model generates a single token. Context priming—pre-loading user history, warming embedding caches, and speculatively fetching tool schemas—is the engineering discipline that actually moves the needle on TTFT.
The Staging Environment Lie: Why Pre-Production Fails for AI Systems
Staging environments give false confidence for AI systems. Here's why they structurally mislead teams—and the production-first architectures that actually work.
Stale Docs, Confident Answers: The Hidden Failure Mode in AI Help Centers
When a RAG system retrieves outdated context, hallucination rates jump sixfold. How to treat documentation freshness as an engineering concern — TTL filtering, temporal reranking, staleness scoring, and the operational model that keeps AI help centers accurate after launch.
When LLMs Grade Their Own Homework: The Feedback Loops Breaking AI Evaluation
LLM-generated eval sets create a feedback loop where model biases get encoded as ground truth. Here are the contamination signals, cross-model validation strategies, and human sampling disciplines that break the loop.
Conflicting Instructions in System Prompts: The Silent Failure Mode No One Owns
System prompts grow through pull requests, accumulating conflicting directives that manifest as unpredictable behavioral drift. Here's how to detect contradictions and architect prompts that survive change.
Tool Call Convergence: Designing Agents That Know When to Stop
Agents that loop through tool calls without a stopping criterion burn tokens for no gain. Here's the engineering discipline for knowing when enough information is enough.
The Two-Speed Organization: Why AI Teams and Product Teams Run on Incompatible Clocks
AI model experimentation takes weeks, product ships in days, embedding indexes update monthly. This clock mismatch is why AI features live in permanent beta — and here's how to fix it.
The Vector Dimension Tax: How Embedding Size Quietly Drains Your Budget
Most teams pick embedding dimensions from model defaults without measuring the cost. Here's how dimensionality affects storage, latency, and quality — and how to make the tradeoff deliberately.
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.
