Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM

An adaptive parsing strategy for enterprise RAG systems improves efficiency by starting with a cheap parser and escalating to more powerful ones only when necessary. This approach uses a cascade of checks to determine if the initial parse is sufficient, with the final line of defense being the LLM evaluating its own input. The process is demonstrated through two real-world examples: escalating a poorly parsed table to Azure Layout and sending a complex figure to a vision LLM for interpretation. This method balances cost and accuracy, ensuring complex document structures are handled correctly without unnecessarily processing simple text with expensive models.