Voxie

I use this and other models to run comparative behavioral analysis across live, production LLMs

I considered several LLM platforms, but ultimately chose ChatGPT for its balance of capability, consistency, and usability.

It’s flexible enough to support different workflows, while remaining predictable and easy to iterate on during real product development.

Highly customizable. Behavior can be shaped through system prompts, examples, and interaction design, with fine-tuning or retrieval added as needed.

Clear constraints and tone definitions matter as much as the prompt text itself.

It can automate many routine tasks reliably when workflows are clearly defined and monitored.

For critical or long-running automation, pairing it with external systems and guardrails is important to maintain consistency and trust.

Yes. You bring custom data via fine-tuning, retrieval (RAG), or system-level instructions, depending on the use case.

Teams produce faster results by shaping behavior through prompts and interaction design before reaching for fine-tuning.