Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
You have a few hundred typed fields to pull out of a stack of real documents: amounts, dates, coverage limits, one structured value per field. The default reflex is to send every field to a GPT-4-class hosted API. It works, and the bill is the largest line in the pipeline’s running cost.
- ▪You have a few hundred typed fields to pull out of a stack of real documents: amounts, dates, coverage limits, one structured value per field.
- ▪The default reflex is to send every field to a GPT-4-class hosted API.
- ▪It works, and the bill is the largest line in the pipeline’s running cost.
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Large Language Models Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship Enterprise Document Intelligence [Vol.1 #8quater] – Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship Kezhan Shi Jul 24, 2026 13 min read Share Photo by Brice Cooper, via Unsplash. You have a few hundred typed fields to pull out of a stack of real documents: amounts, dates, coverage limits, one structured value per field. The default reflex is to send every field to a GPT-4-class hosted API. It works, and the bill is the largest line in the pipeline’s running cost. Most of those fields are plain lookups a much smaller model handles fine, and you are paying flagship prices for all of them.
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