project-development

Assessing task-model fit involves identifying whether a specific task requires LLMs or traditional code. This methodology guides you in evaluating task complexity to determine if large language models are appropriate before committing to a pipeline architecture.

The canonical pipeline architecture for batch processing LLM applications is a staged workflow: acquire, prepare, process, parse, and render. This structure ensures robust data handling and efficient state management throughout the processing lifecycle.

Choosing a multi-agent system over a single-agent system depends on task complexity and required scale. The methodology helps determine the optimal agent architecture by evaluating specific project requirements and cost constraints.

Estimating costs for an LLM-powered project involves calculating anticipated token usage and processing scale. This skill provides frameworks to evaluate expenses, optimize for cost efficiency, and plan iterative development effectively.

Managing state in an LLM pipeline architecture is handled efficiently using the file system. This approach allows structured data persistence across the acquire, prepare, process, parse, and render stages without requiring complex databases.

Iterative LLM development strongly benefits from agent-assisted development and manual prototyping. This methodology emphasizes using agents to refine pipelines and manage file system state for effective project iteration.