About Me
I build reliable AI coding agents at enterprise scale. The answer isn't better models — it's better prompt engineering and systematic evaluation.
My methodology: let friction surface the gaps. Every time I correct an agent twice, that correction becomes a persistent constraint. The result is behavioral specifications across the full Claude Code toolkit — CLAUDE.md, Skills, Rules, Hooks, Commands, Memory — that make agents reliable from the first prompt.
This approach has shipped 4 production applications with compounding velocity. Each project faster than the last as learnings accumulate.
I'm now focused on the next level: moving from task prompting (making agents do X reliably) to behavioral engineering (shaping how agents reason before they act). Task prompts make agents good at specific jobs. Behavioral design makes them reliable at novel problems.
Prompt Engineering Principles
A systematic approach to making AI coding agents reliable at enterprise scale
"I design systems that trigger their own improvement cycles. Friction detection, learning, and replanning happen continuously — not when I remember to ask."
The L3 Thesis — Self-Improving Systems
Every principle below serves this goal: removing the human as the bottleneck for planning, learning, and iteration.
Friction-Driven Refinement
Bootstrap the loop by letting friction surface what's missing. Don't over-engineer prompts upfront — let failures reveal the gaps, then encode the fixes into persistent context.
Compound Learning Loops
Every solved problem gets documented in searchable format. Agents search past solutions before planning. The system accumulates intelligence across sessions — knowledge compounds automatically.
Meta-Prompting
Claude generates the prompts that Claude executes. Design specs, implementation plans, behavioral constraints — all authored by AI, curated by humans. The engineer designs the system that writes itself.
Full-Stack Context Architecture
Leverage the complete Claude Code toolkit: CLAUDE.md for behavioral specs, Skills for capabilities, Rules for constraints, Hooks for automation, Memory for persistence. Context inheritance flows from project root through directory hierarchy.
Empirical Prompt Design
Prompts are hypotheses, not products. Ablation testing reveals load-bearing components. Cross-model validation proves generalization. LLM-as-judge scales evaluation. If you can't measure it, you're guessing.
Tools Over Protocols
Local CLI tools have far less overhead than MCP for agent self-verification. Give agents simple bash tools, not protocol negotiation. Minimize friction in the feedback loop.
Artifacts
Actual prompt engineering work and methodology artifacts
Implementation Prompts
Multi-step Figma-to-React implementation specifications with 1500+ line prompts including component inventories, state machines, and acceptance criteria. Generated via Claude + Figma MCP iteration, executed by Claude Code.
CLAUDE.md Examples
Frontend behavioral specifications with "NEVER" constraints, service layer architecture enforcement, autonomous debugging workflows, and agent delegation triggers that make agents deterministic executors.
Key Insight
The goal isn't to remove agent thinking—it's to shape it. Deterministic specs handle the 80% where execution matters. Behavioral constraints handle the 20% where agents must reason. The art is knowing which is which.
Philosophy
"We are not prompting anymore. We are orchestrating."
Context engineering is greater than prompting. The CLAUDE.md is where the magic lives — it's the constitution that makes agents reliable.
Each project teaches you where humans are doing work agents could do. Find the bottleneck. Give the agent eyes and hands. Encode the learnings. Compound.
The next frontier isn't better prompts—it's designing agent behavior. Not "do X" but "think this way before deciding." L2 prompting tells agents what to do. L3 engineering shapes how they reason. The difference: one makes agents execute, the other makes them reliable at novel tasks.
Education
Bachelor's Degree in Mathematics and Computer Science
University of California at Riverside
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