Key Takeaways
1. A semantic reasoning layer is essential to translate complex raw data into familiar business terms, making GenAI truly insightful.
2. Dynamic knowledge graphs are crucial for GenAI providing the structure, context, and relationships that help AI understand user intent, map synonyms, and infer context.
3. A semantic layer with a context-router ensures the LLM receives only the precise information it needs for a specific task, leading to a significant improvement in GenAI application accuracy.
Many organizations find their GenAI initiatives stumbling at a crucial hurdle: the models often lack the rich, situational business context needed to provide truly insightful and accurate responses. Situational business context refers to the specific circumstances, environmental factors and unique conditions that influence a business’s operations, decisions, and outcomes at a particular point in time. It encompasses market dynamics, technological advancements, regulatory changes, and other considerations. For example, a retail company launching a new product would consider the situational context of current consumer spending habits, such as whether a recession would dictate a focus on value, in addition to competitor offerings and ecommerce trends. A manufacturing company would weigh the impact on production from fluctuating costs of raw materials, global supply chain disruptions, and geopolitical stability. Understanding and adapting to this ever-changing context is crucial for businesses to remain agile, make informed strategic choices, and ultimately achieve success.
The importance of semantic reasoning
In addition to situational business context, to be able to respond to a business query, GenAI requires knowledge of the organization’s specific jargon and processes. This is accomplished using semantic reasoning – a layer that acts as a translator, transforming complex raw data structures, like tables and columns — into the familiar business language that an organization uses daily. While a GenAI model that questions an LLM might know the definition of a data column, it won’t understand that “LOH” in a clinician’s chat means “Length of Hospitalisation.” Semantic reasoning ensures consistency, so “Length of Stay” always has the same meaning, regardless of who is asking or which tool they are using, thereby eliminating duplicate logic and metric drift. While the idea of a semantic layer isn’t new, its evolution has revealed persistent challenges, particularly in serving the dynamic needs of GenAI.
The evolution of Semantic Layers: progress and persistent gaps
Historically, semantic layers have evolved through several stages, each addressing a piece of the puzzle, but none has comprehensively solved the challenge of providing dynamic, situational context for GenAI.
BI Metadata Layers
Early Business Intelligence (BI) platforms, such as Looker with LookML or Tableau’s data model, bundled metadata layers within each reporting tool. While these centralized key performance indicators (KPIs) and joins, their scope was limited to that specific tool. This meant that if a company used multiple BI systems, each model operated in isolation, and any direct query to the data warehouse bypassed these guarantees, leading to inconsistent results.
Warehouse-Centric and Lakehouse-Side Evolution
To overcome these silos, teams began pushing metric logic closer to the data itself within the data platform, using tools like dbt MetricFlow and Cube. These solutions allowed metrics to live in version-controlled YAML, compile to pure SQL, and leverage the warehouse’s security context, significantly reducing duplication and embracing version control. Major data warehouse and lakehouse vendors also introduced their own native solutions, such as Snowflake Semantic Views, which allow users to define logical business tables and reusable metrics directly in Snowflake, inheriting security and being queryable via plain SQL. Similarly, Databricks Unity Catalog Metric Views store KPI definitions alongside Delta tables, powering dashboards, notebooks, and AI agents like LakehouseIQ. While these advancements improved consistency, they primarily focused on numerical metrics and traditional star-schema dimensions, still falling short of capturing the rich, nuanced business context GenAI craves.
Data Catalog and Data Governance Platforms
Parallel to these developments, platforms like Collibra and Atlan focused on cataloging data assets, capturing lineage, and enforcing policies. These solutions often leverage knowledge graphs to store extensive business context, including data ownership, sensitivity, and quality scores, exposing this information via APIs. While invaluable for stewardship and control, their core mandate isn’t to provide live interaction context understanding for GenAI, and while they may offer metric or glossary modules, they don’t fully address the need for situational awareness at the moment of a user’s query.
The critical takeaway is that dashboards, metric layers, and data catalogs have solved structural and governance challenges, but they still leave GenAI models “stumbling” due to a profound lack of situational business context at the precise moment a question is asked. Hard-coding these nuances into prompts is unsustainable and brittle, and storing them in traditional BI metadata layers is simply too shallow to be effective for dynamic AI interactions.
Dynamic Knowledge Graphs
A knowledge graph contributes significantly to a semantic reasoning layer by providing the structure, context, and relationships that make data meaningful and machine-interpretable. The semantic reasoning layer uses the graph to interpret user intent or system queries more accurately, by mapping synonyms or variations to canonical terms, such as “revenue” → “net_sales” and by understanding entity types such as “Paris” as a city versus a person’s name in a different context. The knowledge graph also assists in inferring context. For example, your query asks for “the top products in Q1”, the graph knows to filter by time and product hierarchy. This graph continuously enriches itself from various sources like schemas, dictionaries, and most importantly, every line of conversation, making it the single, executable definition of meaning for every consumer, whether it’s a report, a service, or an automated process.
A solution that enables knowledge insights
Solutions such as GigaSpaces eRAG deliver context richness, using a unified knowledge graph to store context. Within this graph, various entities such as schema entities (tables, columns), rules, dictionaries, value samples and validated Q&A pairs are treated as first-class graph nodes. A unified approach enables knowledge insights, as the graph can pull both the precise numeric definition of a metric and the business jargon that users actually type. The executable logic links data and metrics, and makes the LLM situationally aware at query-time — a feat legacy semantic layers simply cannot achieve. 
eRAG learns and evolves continuously from user interactions. Its conversational loop is designed to clarify ambiguity and convert implicit “tribal knowledge” into governed, reusable logic. When a user asks “Show OOS rate by supplier,” eRAG can clarify, “Just to confirm: OOS stands for Out-of-Stock Rate?” Upon confirmation, it maps “OOS rate” to “Out-of-Stock Rate” and links it to the relevant metric. Future prompts containing “OOS” or “stock-outs” are automatically resolved, reducing confusion and standardizing terminology. If a user states, “Exclude demo customers; we never bill them,” eRAG can confirm, “Always exclude tiers demo and sandbox from revenue?” If the user responds “Yes”, it creates a rule that applies this filter by default to future revenue queries. This reduces manual effort, improves accuracy, and accelerates knowledge capture.
LLM-Ready Retrieval: Just-Enough Context
A major challenge for GenAI applications is context overflow, where feeding too much irrelevant information to the Large Language Model (LLM) degrades accuracy. eRAG’s knowledge graph addresses this by adding a context-router at every step of the pipeline. This ensures the LLM receives only the information it needs for the specific task at hand—no more, no less—leading to a significant jump in GenAI application accuracy.
In essence, eRAG provides a verifiable, self-evolving, and context-aware foundation for enterprise GenAI systems. It moves beyond static data definitions to create a living knowledge base that understands and adapts to the nuances of your business. For organizations looking to truly harness the power of GenAI, eRAG’s semantic reasoning layer isn’t just an improvement; it’s the indispensable bridge between your raw data and intelligent, human-like conversations, ensuring every AI-generated answer is accurate, verifiable, and deeply rooted in your enterprise’s unique business context.
