project-development

Build LLM batch pipelines by separating expensive LLM calls from deterministic steps using staged architectures like acquire, prepare, process, parse, and render. This approach ensures reliable structured outputs and controlled processing costs.

Use LLM agent development when tasks require flexible text processing, but choose deterministic code for predictable logic. Conduct task-model fit checks to evaluate if LLM automation is suitable before investing engineering effort into multi-agent pipeline architectures.

Estimate token costs for multi-agent pipeline architectures by calculating expected token usage per process-stage LLM call and multiplying by your item count. Add buffers for retries to account for output variation and parsing failures during structured output generation.

File-system state machine conventions persist intermediate artifacts to disk to track progress and enable idempotent retries in LLM pipelines. This manages batch processing state and prevents re-running expensive LLM calls after failures.

Harden structured output parsing by enforcing parseable formats and planning for output variation within your pipeline architecture. Use deterministic boundaries between stages to isolate parsing failures and maintain reliable structured results across batch items.

Limitations of LLMs in complex pipeline architectures include unpredictable output variation and high token costs. Mitigate these by enforcing deterministic boundaries, using task-model fit checks, and implementing file-system state management for idempotent retries.