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12 articlesTool Use in Production: Function Calling Patterns That Actually Work
Most LLM agent failures trace back to under-specified tool schemas, not model capability. A practical guide to schema design, error handling, parallel calling, and security for production function calling.
Your AI Product Needs Evals
Most AI products fail not because of the model, but because of missing evaluation systems. A practical guide to building evals from unit tests to human review to A/B testing — and why starting early compounds.
How to Write Fundraising Appeals That Inspire Giving
Craft compelling fundraising appeals that capture attention and inspire action by applying proven psychological principles and practical strategies. Learn how to navigate the critical first moments of reader engagement to ensure your message resonates and prompts giving.
The Unglamorous Work Behind Rapidly Improving AI Products
Most AI teams plateau after launch — not from lack of capability, but from skipping the boring fundamentals: error analysis, custom tooling, domain expert involvement, and experiment-driven roadmaps.
Cloud Agents Are Rewriting How Software Gets Built
AI coding tools have moved from autocomplete to local agents to cloud agents—and each shift changes the fundamental unit of work. Here's what the cloud agent era actually requires from engineers and engineering infrastructure.
LLM-as-a-Judge: A Practical Guide to Building Evaluators That Actually Work
Most LLM evaluation setups are broken by design—wrong metrics, wrong people, wrong methodology. Here's a concrete framework for building LLM judges that actually correlate with quality and catch real regressions.
A Year of Building with LLMs: What the Field Has Actually Learned
Hard-won lessons from teams that have shipped LLM-powered systems into production: why the model is the least durable part of your stack, how to build eval infrastructure that actually works, and when RAG beats finetuning.
Beyond RAG: Hybrid Search, Agentic Retrieval, and the Database Design Decisions That Actually Matter
Pure vector search fails in production when users query exact identifiers, error codes, and named entities. A guide to hybrid search architectures, agentic retrieval patterns, and the database design decisions that follow.
How AI Agents Actually Work: Architecture, Planning, and Failure Modes
A practical breakdown of how AI agents work under the hood — covering tool use, planning patterns, reflection loops, multi-agent coordination, and the five ways plans actually fail in production.
Hard-Won Lessons from Shipping LLM Systems to Production
Practical engineering lessons from shipping LLM systems: why evals come first, why hybrid search beats pure vector retrieval, and why the model is never the moat.
Building LLM Applications for Production: What Actually Breaks
A practical guide to what breaks when you move LLM applications from demo to production—covering inference cost, latency trade-offs, prompting vs RAG vs finetuning decisions, multi-step pipeline failures, evaluation frameworks, and observability.
LLM-Powered Autonomous Agents: The Architecture Behind Real Autonomy
Most teams claiming to run agents in production aren't — only 16% of deployments meet the bar for true autonomy. A breakdown of the planning, memory, and tool-use subsystems that separate real agents from glorified chatbots, and the five failure modes that sink production systems.
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.
