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12 articlesThe Hidden Tax on Your AI Features: What Your Inference Bill Isn't Telling You
Inference is only 20-30% of the true cost of running AI features in production. A full-stack breakdown — from vector DBs and embedding pipelines to human review and prompt engineering labor — and how to build a cost model before launch.
The Human Bottleneck Problem: When Human-in-the-Loop Becomes Your Slowest Microservice
Human-in-the-loop review is often the right safety design — until your reviewers become the slowest microservice in the system. A practical guide to queue design, multi-signal routing, and SLOs that keep human oversight meaningful at scale.
The Hyperparameter Illusion: Why Temperature and Top-P Are the Last Things to Tune
Engineers reach for temperature first when LLM outputs feel wrong. It's almost never the right move. Here's the evidence-backed tuning order that actually moves the needle.
The Inherited AI System Audit: How to Take Ownership of an LLM Feature You Didn't Build
A practical guide for engineers who inherit LLM features without documentation — how to reconstruct intent, audit guardrails, and refactor safely.
Lazy Evaluation in AI Pipelines: Stop Calling the LLM Until You Have To
Only 4.9% of tokens in a typical AI pipeline actually need a large model. A layered lazy evaluation strategy—semantic caching, complexity routing, early exit, and deferred generation—can cut LLM costs by 30–70% without sacrificing quality.
LLM Code Review in Production: Building a Diff Pipeline That Engineers Actually Trust
How to deploy LLMs as a code review layer that reduces review load without creating noise — covering diff preprocessing, false positive budgets, integration patterns, and the metrics that matter.
The Feature Store Pattern for LLM Applications: Stop Retrieving What You Could Precompute
Applying feature store architecture to LLM context assembly cuts retrieval latency, reduces inference cost, and prevents the training-serving skew that quietly degrades model performance.
What Your Fine-Tuned LLM Is Leaking About Its Training Data
Fine-tuned models can expose training data through verbatim extraction, membership inference, and attribute inference attacks — and a $200 budget is enough to demonstrate it. A technical guide to the threat model, differential privacy tradeoffs, output sanitization, and proactive audit methodology for production deployments.
AI Ops Is Not Platform Engineering: How Running LLM Services Breaks Your SRE Playbook
Running LLM services requires a distinct operational discipline from microservices. Here's where your existing SRE playbook transfers, where it fails, and the new runbook categories you don't have yet.
Multi-Model Consensus: When One LLM Isn't Enough to Sign Off
Most AI systems trust a single model and never know when the failure is systematic. Multi-model consensus routes outputs through multiple provider families, surfaces disagreement as a signal, and reduces tail risk in high-stakes decisions.
The Multilingual RAG Retrieval Gap: Why Cross-Lingual Queries Silently Fail Your Vector Search
Monolingual embeddings produce geometrically meaningless similarity scores across languages — here's why this silent failure mode destroys non-English retrieval quality and what to do about it.
The N-Tier Confirmation Cascade: When More Human Approvals Make AI Less Safe
Adding more human approval stages to AI pipelines often produces the opposite of safety — fatigued reviewers rubber-stamp outputs, models learn to game tired annotators, and you pay the overhead of review without getting its benefit.
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.
