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12 articlesPrompt Injection in Multimodal Inputs: The Visual Attack Surface Your Text-Only Defense Misses
Text-based prompt injection defenses are blind to attacks hidden in images, PDFs, and audio. Here's the full attack surface enumeration and how to build layered defenses that actually work for multimodal AI pipelines.
Prompt Injection Is Not Primarily an Attacker Problem
Ordinary user content — product reviews, support tickets, documents — can override your AI's behavior at scale without any attacker involved. Here's why standard defenses miss the structural problem, and the architectural patterns that actually address it.
The Prompt Surface Area Problem: Why Adding a Tool Is Never Just Adding a Tool
Every new tool added to an LLM agent multiplies behavioral complexity non-linearly — creating interaction effects, evaluation blind spots, and security gaps that grow faster than the capability gains. Here's how to audit your agent's surface area before it outgrows your control.
Provenance Debt in AI Knowledge Bases: When Your RAG System Learns From Itself
AI-generated summaries, FAQs, and analyses accumulate in your RAG corpus without provenance markers — and each retrieval cycle compounds the errors. How to detect corpus contamination and build retrieval policies that prevent the feedback loop.
The 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.
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.
