A Systematic Framework for Enterprise Knowledge Retrieval: Enhancing RAG with LLM-Generated Metadata
This forum entry profiles a survey/research paper on using LLM-generated metadata to enhance enterprise RAG retrieval.
Paper Information
| Field | Content | |---|---| | Title | A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systems | | Authors | Pranav Pushkar Mishra, Kranti Prakash Yeole, Ramyashree Keshavamurthy, Mokshit Bharat Surana, Fatemeh Sarayloo | | Published | 2025-12-05 | | Source | https://arxiv.org/abs/2512.05411 | | Type | Survey / empirical framework | | Category | Agentic Search |
What the Paper Does
In enterprise settings, efficiently retrieving relevant information from large and complex knowledge bases is essential for operational productivity and informed decision-making. The paper presents a systematic empirical framework for metadata enrichment using LLMs to enhance document retrieval in RAG systems. A structured pipeline dynamically generates meaningful metadata for document segments, substantially improving their semantic representations and retrieval accuracy.
Experimental Design
A controlled 3 x 3 experimental matrix compares:
- Chunking strategies: semantic, recursive, naive
- Embedding techniques: content-only, TF-IDF weighted, prefix-fusion
- Metadata-enriched approaches consistently outperform content-only baselines.
- Recursive chunking + TF-IDF weighted embeddings: 82.5% precision.
- Naive chunking + prefix-fusion: strongest ranking quality (NDCG 0.813).
- Latency: sub-30 ms P95, making the approach deployable in production.
- Dense retrieval: high recall, low latency; suited for first-stage retrieval.
- Late interaction (e.g., ColBERT): higher precision but larger indexes.
- Generative IR: directly "generating" documents by tokens/docids, simplifying cascades.
- Agentic search: modeling retrieval as sequential decision-making with multi-hop reasoning and self-reflection.
Ablation analysis isolates each component's contribution. Ground truth is generated via cross-encoder reranking, with statistical significance confirmed through Bonferroni-corrected paired t-tests.
Key Results
Context in the Field
The forum post situates the work within four main research threads:
Engineering Checklist
| Concern | Question | Recommendation | |---|---|---| | Data | PII in training/index? Versioning? | Partitioned indexes, sanitization, rollback-able embedding versions | | Latency | p99 budget? Retrieval steps? | Cascade + early stopping, caching hot queries, async reranking | | Quality | Do offline gains translate online? | Interleaving experiments, human audits, citation checks | | Security | Poisoning/bias via open retrieval? | Source whitelisting, adversarial detection, output filtering | | Cost | Per-query tokens and GPU usage? | Small-model routing, distillation, hybrid sparse+dense |
Takeaways
1. Architecture: cascade retrieval + reranking + generation remains mainstream, but agentic paradigms make retrieval strategy itself learnable. 2. Data: high-quality instruction data and interaction logs matter; synthetic data requires leakage/distribution checks. 3. Evaluation: offline-online gaps widen; LLM-as-judge needs cross-validation with human assessment. 4. Product: latency, cost, explainability, and safety are hard constraints for production deployment.
Original Abstract (verbatim)
> In enterprise settings, efficiently retrieving relevant information from large and complex knowledge bases is essential for operational productivity and informed decision-making. This research presents a systematic empirical framework for metadata enrichment using large language models (LLMs) to enhance document retrieval in Retrieval-Augmented Generation (RAG) systems. Our approach employs a structured pipeline that dynamically generates meaningful metadata for document segments, substantially improving their semantic representations and retrieval accuracy. Through a controlled 3 X 3 experimental matrix, we compare three chunking strategies -- semantic, recursive, and naive -- and evaluate their interactions with three embedding techniques -- content-only, TF-IDF weighted, and prefix-fusion -- isolating the contribution of each component through ablation analysis. The results demonstrate that metadata-enriched approaches consistently outperform content-only baselines, with recursive chunking paired with TF-IDF weighted embeddings yielding 82.5% precision and naive chunking with prefix-fusion achieving the strongest ranking quality (NDCG 0.813). Our evaluation employs cross-encoder reranking for silver-standard ground truth generation, with statistical significance confirmed via Bonferroni-corrected paired t-tests. These findings confirm that metadata enrichment improves vector space organization and retrieval effectiveness while maintaining sub-30 ms P95 latency, providing a quantitative decision framework for deploying high-performance, scalable RAG systems in enterprise settings.