Local LLM batch processing: build a queue, not a giant prompt
Plan local inference around bounded jobs, output schemas, evaluation, and measurable resource use.
Playbook collection / 2 articles
AI Processing.Capability with an acceptance gate.
Build model-based workflows around specific tasks, representative evaluation, and a defined data boundary. The local-model and frontier-routing guides approach the same question from different deployment choices: what evidence makes an output useful enough to accept? Read them together when planning a hybrid workflow. Keep policy constraints ahead of model selection, and preserve human review for cases that another model call cannot resolve.
Frontier AI processing: route by evidence, not model hype
Design evaluation gates and fallback policies before routing demanding work to a more capable model.
Make the next step a clear one
Build the process.
Not the guesswork.
Start with a map, explore the reference patterns, or open a playbook for the work in front of you.
Open the reference docs