Page 48
12 articlesThe AI On-Call Playbook: Incident Response When the Bug Is a Bad Prediction
Traditional runbooks break when the symptom is 'outputs feel wrong.' A practical triage decision tree, escalation criteria, and postmortem format built specifically for AI systems in production.
The AI Ops Dashboard Nobody Builds Until It's Too Late
Latency and error rate cover less than 20% of the failure space for LLM-powered features. Here are the five production failure modes your APM dashboard silently ignores — and the signal hierarchy that actually catches them.
Chatbot, Copilot, or Agent: The Taxonomy That Changes Your Architecture
Picking the wrong AI interaction paradigm — chatbot, copilot, or agent — creates architectural debt you can't fix by tuning prompts. A breakdown of the trust models, context-window strategies, and error-recovery requirements that should drive the decision before you write a line of code.
The Cold Start Problem in AI Personalization: Being Useful Before You Have Data
New users have no history, your model has no context, and you're competing against the perception that AI doesn't know them. Here's the engineering playbook for bridging that gap.
Why '92% Accurate' Is Almost Always a Lie
A single accuracy number hides the errors that actually matter. Here's a four-dimension taxonomy — correct, recoverable, harmful, abstained — and a one-page format that gives non-technical stakeholders enough to make the right product, legal, and investment decisions.
The Data Flywheel Is Not Free: Engineering Feedback Loops That Actually Improve Your AI Product
Most teams collect thumbs-up/down and call it a feedback loop. The real infrastructure is implicit signal extraction, weak supervision pipelines, and closed-loop architecture that routes production data back into training without drowning in annotation overhead.
Data 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.
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.
