The Fifth Elephant 2026 Annual Conference

Large Language Models are powerful at reasoning and recommendation, but they routinely hallucinate entities that do not exist in an organization’s proprietary data. The common response is to build a Retrieval-Augmented Generation (RAG) stack with embeddings, a vector database, and retrieval infrastructure. However, many enterprise datasets are not collections of long documents—they are structured master data such as product catalogs, audience segments, taxonomies, brands, and reference lists containing thousands of short records. In our case, we needed to ground an agent’s recommendations in a proprietary catalog while keeping latency, operational complexity, and deployment footprint low.

This session presents a practical evaluation of lightweight retrieval approaches for agent grounding without a vector database. We benchmarked BM25 variants, MiniLM semantic embeddings, and Model2Vec static embeddings across real-world planning queries and evaluated them not only on recall, but also on a “trap rate” metric that measures how often retrieval surfaces wrong-but-plausible results that could mislead a downstream LLM. The findings were surprising: lightweight static embeddings came within a point of transformer-based semantic retrieval while requiring a fraction of the runtime footprint, and enriching records with descriptions often increased the number of confusable false positives without significantly improving recall. We will share the evaluation methodology, results, and a practical framework for selecting retrieval strategies based on how agents consume proprietary data.

Key Takeaways

• You may not need a vector database to ground agents against structured enterprise data. Lightweight approaches such as BM25 and static embeddings can achieve comparable retrieval quality with significantly lower operational overhead, latency, and deployment complexity.
• Measure retrieval safety, not just retrieval accuracy. Improving recall is only part of the problem; wrong-but-plausible retrieval results can be more harmful to agent behavior than obvious misses. Understanding and measuring these “trap” results leads to more reliable grounded agents.

Intended Audience

This session will be valuable for:

• AI/ML Engineers building agentic applications and LLM-powered workflows
• Platform and Infrastructure Engineers evaluating RAG architectures
• Data Scientists and Applied AI practitioners working with proprietary enterprise data
• Product Engineers integrating LLMs with catalogs, taxonomies, reference data, or recommendation systems
• Engineering leaders looking for pragmatic alternatives to complex vector database deployments

Bio

Pranav is a software developer, Solution Consultant at Sahaj Software. specializing in agentic AI systems and AI engineering. He shares insights from building intelligent AI applications, explores how AI is reshaping software development, and helps teams translate emerging AI capabilities into practical, production-ready solutions.
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