project-development

Build a staged pipeline covering acquire, prepare, process, parse, and render stages to support deterministic iteration. This file-system state architecture ensures robust batch processing and controlled experimentation for LLM-driven workflows.

Evaluate task-model fit by running a manual prototype on a representative task sample. This validation determines whether an item should be solved with LLMs before committing to full pipeline architecture and development.

Estimate LLM prototype costs by running a manual prototype on representative data, then outlining a minimal staged pipeline. This approach provides concrete architecture tradeoffs for accurate cost and timeline estimation.

Building a multi-agent research system requires a file-system state to support deterministic iteration. This architecture decision ensures robust, scalable workflows for interactive agent applications and batch pipelines.

Limitations of using LLMs for interactive agent applications include architecture tradeoffs in scalability and cost. Without a manual prototype and staged pipeline, deterministic iteration and controlled experimentation become difficult to maintain.