Why use it? How to implement it? What can we do when it fails?

The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them

The post Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context

The post Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol.1 #5octies] - Rules propose, LLM validates: six deterministic signals on span-level typography surface heading candidates, one bounded loop keeps the real ones, and the same toc_df drops back into the RAG pipeline

The post Building Document Structure with Loop Engineering: Recovering a PDF’s Outline from Body Typography for RAG appeared first on Towards Data Science.

A hybrid LLM application pattern that combines a predefined workflow with adaptive agent behavior

The post Put the Agent Inside the Workflow appeared first on Towards Data Science.