project-development

To evaluate task-model fit, validate your core task with a manual prototype to determine if LLMs are suitable versus traditional code. This prevents costly architectural mistakes before committing to full agent development.

Design robust, staged pipelines using file-system-based state management for batch processing. This modular approach supports iterative agent-assisted workflows and ensures scalable LLM pipeline architecture.

Estimate LLM project costs by predicting and managing expenses after validating task-model fit and designing your pipeline architecture. This structured methodology ensures cost-effective implementation throughout development.

Use traditional code instead of an LLM when task-model fit evaluation shows the task is unsuitable for probabilistic processing. Validating the core task with a manual prototype helps determine if deterministic code is more efficient.

Yes, you can manage LLM agent state using the file system. The methodology emphasizes file-system-based state management to design robust, staged pipelines for iterative development within agent-assisted workflows.