Enterprises require the information located in their structured databases for decision-making, and to gain business insights. Unfortunately, querying this data comes with a host of challenges, from high latency and cost concerns, to complex data relationships. Organizations that have onboarded GenAI often discover that most modern foundation models aren’t quite up to the task of understanding the context of complex structured business data.

Without real context, large language models (LLMs) produce inaccurate responses, which can put the business at risk. Since most enterprise databases store thousands or even millions of table objects, often without full documentation or descriptions, the significance of these objects is not obvious to LLMs. Fortunately, several specialized technologies have emerged to help address these issues, including Cache Augmented Generation (CAG), Table Augmented Generation (TAG), and Agentic Retrieval-Augmented Generation (Agentic RAG). Each of these solutions plays a unique role in enhancing structured data querying. Let’s examine each in more detail. 

Cache Augmented Generation (CAG) 

CAG was designed to enhance the efficiency of RAG by adding a caching layer that reduces the need to repeatedly query databases for frequently accessed information. This improvement optimizes performance, reduces costs, and streamlines data interaction. 

One of CAG’s key features is Response Caching, where frequently accessed information or prior inference states are stored to avoid redundant retrieval or computation. This ensures rapid and consistent responses without repeated expensive operations. 

Response Caching is implemented in these ways:

• Static Response Caching: enhances both speed and reliability, by ensuring that once the relevant data is loaded, that it remains consistent across interactions without requiring repeated retrieval
• Inference State Caching: stores previous inference states to avoid redundant computations, saving computational resources and improving performance 

The Key Benefits of CAG 

• Enhanced User Experience: Provides quicker, seamless interactions that increase user satisfaction.
• Latency Reduction: CAG retrieves commonly queried data from an in-memory cache, significantly reducing response time
• Cost Efficiency: By lowering database query volumes, CAG lessens computational and storage costs
• Scalability: This approach allows systems to scale efficiently, especially in environments with high data demand 

However, while CAG boosts performance, it fails to address the challenges related to data relationships and integration across many different structured data sources. 

Table Augmented Generation (TAG) 

TAG compliments RAG by focusing on structured data within databases. While RAG mainly handles semi-structured or unstructured text from document repositories—retrieving relevant passages before a language model generates responses—TAG uses SQL-based querying to extract specific rows and columns from relational tables. It then enhances these results with advanced AI-driven insights, such as anomaly detection, trend analysis, and predictive forecasting

This approach is particularly valuable in industries where data accuracy and precision are paramount. For instance, in finance, real-time visibility into transactions or regulatory metrics is essential. In retail, businesses need to analyze sales by region, SKU, or season. TAG retrieves precise figures and provides AI-enhanced insights into historical patterns and future trends, to make sure responses are data-driven and contextually relevant. 

The Key Benefits of TAG

• Improved Data Understanding: TAG helps AI systems interpret relationships between tables and columns, improving query accuracy
• Cross-System Data Integration: It facilitates querying across multiple enterprise systems, reducing data silos 
• Accessibility for Non-Experts: TAG enables natural language querying, removing the need for users to have extensive SQL knowledge 

While TAG improves structured data retrieval, it struggles to integrate unstructured data and needs enhancements for complex data interactions across diverse systems. 

Agentic RAG 

Agentic RAG is an advanced AI approach that uses innovative techniques to improve the retrieval and generation of contextually relevant information. It builds on traditional RAG by incorporating autonomous decision-making into its architecture. This enables the system to proactively refine queries, adjust responses, and iteratively enhance results, creating a more dynamic and context-aware user experience. 

The Agentic RAG architecture aims to replicate aspects of human reasoning by integrating retrieval processes with autonomous decision-making loops. Rather than simply linking large language models (LLMs) with knowledge bases, it establishes a feedback loop that allows the system to self-assess and refine its outputs. It actively participates in decision-making, using real-time feedback and adaptive algorithms to modify its approach as needed. For instance, in traditional RAG models, if a query presents incomplete results, the system cannot self-correct without a user intervening. On the other hand, an agentic RAG application is able to autonomously reframe the query or look for alternative knowledge sources to provide a more comprehensive response. 

This advancement positions agentic RAG as a strong foundation for applications demanding both high accuracy and contextual relevance.  

The Key Benefits of Agentic RAG 

• Contextual Data Interpretation: AI agents understand the real meaning behind data structures, which adds context to queries 
• Optimized Query Processing: By pinpointing the most relevant data sources, Agentic RAG improves accuracy and efficiency 
• Enterprise-Wide Data Integration: It also allows entities to query data across a host of structured databases seamlessly

Despite these advancements, Agentic RAG has issues handling highly complex or ambiguous queries involving a mix of structured and unstructured data sources.

A Comprehensive Solution for Querying Structured Enterprise Data 

The way to overcome the limitations of conventional data querying approaches is to leverage multi-agentic RAG. This approach overcomes the limitations of traditional data querying by enhancing natural language interactions with structured enterprise data. Unlike standard caching or table-augmented techniques, multi-agentic RAG ensures real-time, trustworthy insights without AI hallucinations, making data more accessible and actionable across multiple sources. Integrating this technology with an intuitive, conversational interface allows users to interact naturally, benefiting from automated onboarding and seamless integration for precise, contextual answers. 

How Multi-Agentic RAG Solves Querying Challenges 

Multi-agentic RAG improves traditional querying by optimizing retrieval and enhancing contextual understanding. 

• Enhanced Data Understanding: Use AI to interpret cryptic table and column names, making structured data more sensible and usable 
• Enterprise-Level Scalability: Designed for high-performance environments, it optimizes query efficiency and reduces LLM consumption costs
• Contextual Reasoning: Extracts logic from various sources, ensuring data is interconnected and fully understood within the enterprise context
• Natural Language Querying: Users can extract meaningful insights without technical expertise, as AI-driven reasoning delivers relevant responses to complex queries, making the right information available when and where it’s needed

Last Words 

The landscape of structured enterprise data querying is shifting, with solutions like CAG, TAG, and Agentic RAG improving efficiency and accuracy. GigaSpaces eRAG takes this a step further by rapidly connecting users to multiple databases so users can unlock the value of operational data across the business and gain deeper insights to enhance business decision-making. Discover how eRAG transforms structured data querying for enterprise AI. Download this eBook  to explore its capabilities in detail.