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12 articlesData Versioning for AI: The Dataset-Model Coupling Problem Teams Discover Too Late
Why 'the model regressed' usually means 'the upstream data changed' — and the lineage graph patterns that let you trace production degradations to their data cause before wasting a week re-tuning prompts.
The Implicit Feedback Trap: Why Engagement Metrics Lie About AI Quality
Thumbs-up ratings, click-through rates, and satisfaction scores are systematically biased toward confident-sounding AI outputs — not accurate ones. Here's why engagement metrics make AI worse over time, and which behavioral signals actually track quality.
Knowledge Graph vs. Vector Store: Choosing Your Retrieval Primitive
Vector similarity and graph traversal answer different questions. Learn when vector stores fail on multi-hop reasoning, when knowledge graphs win on structured queries, and how to build hybrid retrieval that handles both.
The LLM Local Development Loop: Fast Iteration Without Burning Your API Budget
How to build a fast inner loop for LLM applications using record-replay patterns, deterministic fixtures, and a layered test strategy — without burning your API budget on every code change.
The LLM Pipeline Monolith vs. Chain Trade-off: When Task Decomposition Helps and When It Hurts
Most teams default to chaining LLM calls without measuring whether it beats a single large-context call. Here's what the empirical evidence actually says about when to chain and when to go monolith.
Model Deprecation Readiness: Auditing Your Behavioral Dependency Before the 90-Day Countdown
When a model gets deprecated, the hard part isn't updating the API call — it's discovering all the invisible behavioral contracts your system assumed. Here's how to audit them before the clock runs out.
Model Routing in Production: When the Router Costs More Than It Saves
Most teams deploy model routers expecting automatic cost savings. The counterintuitive reality: a poorly designed router can cost more than sending every request to the expensive model. Here's the decision framework that actually works.
How to Pick the Right LLM Before You Write a Single Prompt
Public benchmarks have saturated and can't tell you which LLM will work in your system. A practical framework for evaluating models on the dimensions that actually matter: function-call reliability, structured output compliance, refusal rate on your domain, and latency under real concurrency.
Preference Data on a Budget: Capturing RLHF Signal Without a Research Team
How to collect pairwise preference signal from real users using implicit behavioral telemetry, inline editing, and A/B prompts — plus the minimum viable reward model setup that works without PPO infrastructure.
Prompt Injection at Scale: Defending Agentic Pipelines Against Hostile Content
Prompt injection is the #1 vulnerability in production AI agents. Here's the attack surface, why instruction-level defenses fail, and the architecture that keeps systems useful under adversarial pressure.
Prompt Regression Tests That Actually Block PRs
Most teams claim to test their prompts. Almost none have CI gates that will fail a build. Here's the lightweight harness that changes that without burning your API budget.
Retrieval Debt: Why Your RAG Pipeline Degrades Silently Over Time
Your RAG pipeline was working fine at launch. Now answers feel slightly off and nobody can explain why. Here's how retrieval debt accumulates through stale embeddings, tombstoned chunks, and encoder drift — and how to stop it before users notice.
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.
