Plan, Review, Execute: Getting Better Results from LLMs
This blog documents learnings from building Angzarr—a polyglot event sourcing framework. The framework core is written in Rust, so examples here are primarily Rust.
Angzarr doesn't require Rust. Client SDKs exist for Go, Python, Java, C#, and C++. The author—a polyglot developer—doesn't believe Rust is the best language for everything. It is the right choice for this framework's core, and building it has produced these learnings.
The Rust should be readable by most programmers. If you have questions: consult The Rust Book, ask an LLM, or email the author.
The most effective LLM workflows share one trait: they force a pause between planning and execution. You wouldn't let a contractor start demolition before approving blueprints. The same applies to AI assistants.