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12 articlesThe Quiet Quitter Pattern: Why Your AI Engagement Metrics Are Lying to You
Users who click AI suggestions and immediately rewrite them look identical to genuinely engaged users in your analytics. Here's how to measure what's actually happening.
Quota Starvation: When Your AI Features Eat Each Other's Rate Limits
When multiple AI features share a single API key, priority is set implicitly by whoever issues requests first. Here's how to make quota allocation explicit before a batch job starves your user-facing features.
The RAG Eval Invalidation Paradox: Why Updating Your Knowledge Base Breaks Your Benchmarks
Updating your RAG knowledge base doesn't just change what your system retrieves — it silently invalidates the evaluation sets you're using to measure it. Most teams never realize the difference.
The Data Contract Problem in RAG: When Your Ingestion Pipeline Silently Breaks Retrieval Quality
Schema drift, embedding model updates, and stale documents can silently degrade RAG retrieval quality for weeks without a single error log. Here's how data contracts and ingestion-layer monitoring stop the rot before users notice.
Rate Limits Are a Design Constraint, Not an Error Code
When LLM API rate limits are treated as edge cases rather than architectural constraints, the results range from silent cost explosions to complete service failures. Here's how to design systems that function under sustained quota pressure.
Your Refusal Logs Are a Product Backlog in Disguise
The requests your AI refuses are the most honest user research data you have. Here's how to read them like a product backlog instead of a security watchlist.
The Retrograde Accuracy Problem: Why AI Features Degrade as Your Product Grows
AI features that launch at 91% accuracy can quietly erode to 83% six months later — not from model drift, but because product complexity creates input states the model was never trained on. How to detect it, audit for it, and close the gap before users notice.
Scheduling Fairness in Multi-Tenant LLM Inference: Why FIFO Is the Wrong Default
When multiple teams share LLM inference infrastructure, naive FIFO scheduling causes priority inversion and SLO violations. Here's what fair scheduling actually looks like in production.
Your 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.
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.
