Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
The article describes an adaptive parsing strategy for enterprise document intelligence that starts with a cheap parser and escalates to more powerful parsers only when needed. It adds a large language model as a final check to catch errors that deterministic checks miss, using self‑evaluation and groundedness checks. Two real‑world examples—escalating a flat table to Azure Layout and a figure to a vision LLM—demonstrate the cost‑effective, loop‑engineered approach.
- ▪Adaptive parsing begins with a fast, inexpensive parser and escalates to deeper parsers based on a cascade of checks.
- ▪Deterministic checks flag obvious parsing failures, while the LLM’s self‑evaluation and groundedness checks catch subtler errors at generation time.
- ▪The article walks through two end‑to‑end escalations: a flat table parsed with Azure Layout and a diagram parsed with a vision LLM.
- ▪By escalating only when necessary, the system reduces computational cost compared to running the heaviest parser on every page.
Opening excerpt (first ~120 words) tap to expand
Large Language Models Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM Enterprise Document Intelligence [Vol.1 #10B] – The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model angela shi Jul 20, 2026 25 min read Share Photo by Larry Hyler, via Pexels. Some bad parses do not look bad until the answer comes out (classic OCR is the textbook case: EasyOCR recovers the words and quietly drops the table structure around them, and the answer reads fine until you check it against the page).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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