Declare, Constrain, Verify: Building Reliable Systems with Claude Code

Many developers approach Claude Code like a chatbot — provide a prompt and expect reliable results. While this may work for simple tasks, it often breaks down when correctness, determinism, and reliability become critical.

In this session, we’ll explore a more disciplined approach to working with LLM systems: declaring truth upfront, constraining models through enforceable rules, and verifying outputs with deterministic systems. Through a practical demonstration, you’ll see how these principles can be applied in a real-world pipeline.

The session features a receipt validation workflow built across multiple layers: a JSON schema acting as a contract, Claude Code generating test cases within hook-enforced boundaries, and a Rust binary performing deterministic validation. Along the way, we’ll explore practical Claude Code primitives such as skills, hooks, slash commands, and headless mode that can be directly applied to production workflows.

This is a hands-on, systems-oriented session focused on building more reliable and verifiable AI-assisted workflows.

Key Takeaways:

• Reliable LLM Workflows – moving beyond chat-style interactions
• Declare, Constrain, Verify – core principles for dependable AI systems
• Structured Contracts – using schemas to enforce correctness
• Deterministic Validation – combining LLMs with traditional systems
• Claude Code Primitives – hooks, skills, slash commands, and headless mode
• Practical Pipeline Design – building robust AI-assisted tooling

Prerequisites:

• Ability to code in Python

Delaney is an engineer at Insly, building AI-assisted tooling and insurance product implementations. They write about disciplined LLM workflows and minimalist systems design.