Hybrid system for evidence-grounded narrative generation with structure-preserving document processing and quality-gated retrieval

A system for generating evidence-grounded narrative content, comprising:

• one or more processors;
• and
• a memory storing instructions that, when executed by the one or more processors, cause the system to:
• process a plurality of source documents to extract text content while preserving structural relationships including table structures;
• generate vector embeddings for document chunks derived from the processed source documents and store the vector embeddings with document metadata in a data store;
• receive a generation request comprising customer segment information;
• retrieve candidate document chunks from the data store using a multi-factor ranking algorithm that combines semantic similarity with context-based boost factors including at least a document type factor and a product type alignment factor;
• generate narrative content using a language model with the retrieved candidate document chunks as context, wherein the language model is configured to provide document identifiers for source documents referenced in the generated narrative content;
• validate that each provided document identifier corresponds to a document chunk that was included in the context provided to the language model;
• evaluate the generated narrative content to determine whether the narrative content is grounded in evidence from the retrieved document chunks;
• and
• output the generated narrative content when the evaluation indicates the narrative content satisfies a predetermined quality criterion.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• ingesting documents and extracting structured content while preserving document structure including table relationships;
• generating embeddings for document segments and storing the embeddings with metadata indicating document type, product type, and content category;
• receiving a request to generate product-related content for a specified customer segment;
• retrieving relevant document segments using a composite scoring function that applies configurable boost values to segments matching specified metadata criteria;
• generating content using a language model with the retrieved document segments as grounding context;
• analyzing the generated content to determine a quality score indicating whether the generated content is grounded in evidence from the retrieved document segments;
• and
• controlling output of the generated content based on the quality score, wherein content failing to meet a predetermined quality threshold is either regenerated with feedback or flagged for review.