Retrieval-Augmented Generation (RAG) systems are fast becoming the connective tissue between Large Language Models (LLMs) and real-world business data. But while RAG systems excel at fetching relevant passages from documents, they often stumble when the data isn’t narrative but numerical. In enterprise environments, where structured formats like HR tables, policy records, or financial reports dominate, this mismatch has become a bottleneck.
The paper “Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data” by Chandana Cheerla proposes a much-needed upgrade: a RAG system that treats structured and tabular data as first-class citizens. It doesn’t just flatten tables into linear strings or hope LLMs can reason through semi-garbled inputs. It restructures the entire RAG pipeline to respect and preserve the meaning of tables, rows, and metadata.
Why Existing RAG Falls Short in Enterprise Settings
Most RAG pipelines are built for unstructured natural language, not enterprise-grade data. The standard approach chunkifies documents based on token length and feeds them into embedding models like all-mpnet-base-v2 for dense retrieval. But this leads to three big problems in enterprise use:
| Limitation | Problem in Enterprise Context |
|---|---|
| Flat Text Chunking | Fractures table meaning and row-column logic |
| Semantic-only Retrieval | Misses exact match requirements for regulatory or financial data |
| No Reranking | Cannot prioritize the most contextually relevant answers |
The Paper’s Fix: A Structured, Hybrid, Human-in-the-Loop RAG Pipeline
The paper outlines a robust pipeline that addresses these issues head-on. Here’s a breakdown of its most notable contributions:
1. Structure-Aware Chunking
- Textual content is split semantically using a recursive character splitter.
- Tabular data is extracted with tools like Camelot and Azure Document Intelligence, preserving row-column structure in JSON.
- Each row is indexed separately in FAISS, enabling pinpoint answers to row-specific queries.
2. Hybrid Retrieval with Reranking
- Combines dense semantic embeddings (60%) and BM25 sparse retrieval (40%) for balanced relevance.
- Introduces cross-encoder reranking using MS MARCO models to reorder top chunks based on query alignment.
3. Metadata Filtering and Entity-Aware Search
- Integrates SpaCy NER to tag chunks with metadata (e.g. department, confidentiality, dates).
- Enables entity-aligned filtering to make queries like “Get policies by Finance in 2023” actually work.
4. Feedback Loop with Query Reformulation
- Uses LLaMA and Mistral to rewrite vague queries.
- User thumbs-down triggers automatic query expansion and retry.
- Maintains a 10-turn conversational memory for session continuity.
Benchmark Results That Matter
The paper doesn’t just propose a slick architecture. It backs it with enterprise-focused benchmarks:
| Metric | Direct LLM | Naive RAG | Advanced RAG |
|---|---|---|---|
| Precision@5 | 62% | 75% | 90% |
| Recall@5 | 58% | 74% | 87% |
| MRR | 0.60 | 0.69 | 0.85 |
| Faithfulness (5pt) | 2.8 | 3.0 | 4.6 |
| Completeness | 2.3 | 2.5 | 4.2 |
| Relevance | 2.9 | 3.2 | 4.5 |
The shift is dramatic. By handling structure and semantics together, the new RAG engine finally delivers answers that are both accurate and faithful to source — no more hallucinated HR policies or misquoted pay scales.
Where This Matters Most: Use Cases
- HR Policy Retrieval: “Show vacation policy for senior engineers hired in 2022.”
- Financial Audits: “List all expense line items over $5,000 in Q3 reports.”
- Compliance Checks: “Find GDPR-related clauses in external vendor contracts.”
In all these examples, the ability to retain tabular logic, perform hybrid matching, and respond to ambiguous queries makes or breaks the usefulness of RAG.
Beyond Tables: What Comes Next?
The authors propose next steps that align with where enterprise AI is heading:
- Dynamic Indexing: So updates to HR records don’t require full reindexing.
- Multimodal Support: Integrate scans, PDFs, charts, and even voice logs.
- Agentic RAG: Use reasoning agents (like ReAct or Reflexion) to choose the best retrieval and rewriting strategies.
Final Thought
This paper quietly solves one of the most ignored pain points in real-world LLM applications: structured data isn’t a footnote; it’s the main act. As more businesses deploy internal LLM chatbots, those that can query Excel-like logic as fluently as they summarize text will define the next leap in enterprise automation.
Cognaptus: Automate the Present, Incubate the Future.