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12 articlesPricing AI Features: The Unit Economics Framework Engineering Teams Always Skip
Variable inference costs break fixed-price SaaS assumptions. A practical framework for per-workflow cost modeling, heavy-user subsidy math, and consumption cap design that preserves margin as usage scales.
Prompt Cache Break-Even: The Exact Math on When Provider-Side Prefix Caching Actually Pays Off
Prompt caching advertises a 90% discount on cache hits, but the write premium means low hit rates cost you more than no caching at all. Here's the exact math and the session architecture decisions that determine whether you capture the discount.
Prompt Canaries: The Deployment Primitive Your AI Team Is Missing
Code canary deployments catch crashes and latency regressions — but they're blind to the behavioral failures that actually hurt LLM systems. Here's the metric stack, deployment manifest pattern, and auto-rollback design that closes the gap.
Prompt Injection Detection at 100,000 Requests Per Day: Why Simple Defenses Break and What Actually Works
Static filters and LLM-as-judge approaches both fail at high throughput. Here's the layered classifier architecture that actually catches prompt injections under a 200ms latency budget.
The Prompt-Model Coupling Trap: Why Your Prompts Only Speak One Model's Dialect
Carefully tuned prompts silently accumulate dependencies on specific model behaviors — JSON formatting quirks, instruction hierarchy, refusal thresholds — that break on migration day. How to build a portability test harness and write lower-coupling prompts.
Property-Based Testing for LLM Outputs: Finding the Bugs Your Eval Set Never Imagined
Curated eval sets encode only the failure modes you imagined. Property-based testing generates thousands of adversarial input variants to find the bugs at domain boundaries your test suite structurally cannot reach.
Poisoned at the Source: RAG Corpus Decay and Data Governance for Vector Stores
Production RAG systems silently degrade as corpora accumulate stale chunks, conflicting facts, and adversarially-crafted content. Here's how to treat your retrieval layer as infrastructure — with TTL design, ingest-time conflict detection, and access control patterns that keep it trustworthy.
The RAG Eval Antipattern That Hides Retriever Bugs
Most teams evaluate RAG systems end-to-end, letting the generator mask retrieval failures. Here's how to build a retriever-only eval harness that surfaces bugs before they compound.
Schema-First AI Development: Define Output Contracts Before You Write Prompts
Naive JSON prompting fails 15–20% of the time in production. Schema-first development — defining output contracts before writing prompts — cuts that to near zero, and the approach is now the right default for every automated LLM pipeline.
The Schema Problem: Taming LLM Output in Production
Structured outputs from LLMs feel solved until version drift, optional fields, and downstream parsers collide. A practical framework for versioning and validating LLM output contracts so a model upgrade never silently corrupts your data pipeline.
The Discovery Problem: Why Semantic Search Fails Browsing Users
Embedding-based retrieval optimizes for users who know what they want. It quietly fails everyone else — here's how to detect browsing intent and fix your ranking strategy.
Semantic Search as a Product: What Changes When Retrieval Understands Intent
Building user-facing semantic search is a different problem than building a RAG pipeline. Half the failures happen before any vector is touched — here's what breaks and how to fix it.
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.
