If you’ve been following recent developments related to semantic layers for Large Language Models (LLMs), you’ll know that there is a lot of excitement building about the significant promise shown in the technology’s enhancement of accuracy, reliability, and contextual understanding of AI-powered analytics.
These advancements are reshaping how businesses interact with their data and leverage AI for decision-making. But before we dig into the benefits, let’s first dive in to explain what is a semantic layer.
What is a semantic layer?
If you’re not familiar, the semantic layer in the context of Large Language Models (LLMs) (such as the GPT model used in the popular ChatGPT product) refers to a layer that helps bridge the gap between raw data and meaningful insights. In simple terms, it’s a system that adds understanding or context to data, allowing LLMs to process and interpret it more effectively.
Here's how a semantic layer for LLMs works:
- Raw Data: In any huge database filled with numbers, text, or records, this data is often unstructured or raw, meaning it is in its most basic form without any organization or classification, and therefore not immediately useful for analysis or decision-making.
- Understanding the Meaning: The semantic layer interprets this data, giving it meaning and context. For example, it can recognize that a number like "9" in one context might refer to a rating on a scale, while in another context, it could represent an amount of money or even a number of customers at a business.
- Connecting Data: The semantic layer helps the LLM system make connections between different pieces of data. For example, it might recognize that "sales" and "revenue" are related concepts, even if they are stored in different parts of the data.
Why is a semantic layer important in data analytics?
So, why is this important in the data analytics industry? There are several reasons. First, by adding meaning to the data, the semantic layer helps analysts and decision-makers interpret information more accurately and quickly. They can ask questions in natural language (like, "What was the total revenue last quarter?") and get precise, context-aware answers.
Another reason is that data is often stored in various formats or structures, but the semantic layer provides a unified view, making sure that anyone–whether they are data scientists, analysts, or non-technical business users–are working with the same understanding of the data.
Additionally, with LLMs, the semantic layer allows for more automation in generating insights. Instead of manually analyzing data, LLMs can use the semantic layer to automatically process and interpret it, leading to faster reports and decisions. When companies grow, their data also becomes more complex, so the semantic layer helps ensure that LLMs can still process vast amounts of data while maintaining an accurate understanding of what it all means.
Without access to a semantic layer in an LLM, a non-technical business analyst trying to understand sales performance would need to manually review rows and rows of raw data to make connections and extract meaningful insights. With a semantic layer, you can instead simply ask a data analytics system powered by an LLM, "How did our sales perform last quarter compared to the previous quarter?" and get a contextually accurate answer that includes trends, key factors, and comparisons.
Another way to think of the semantic layer is like a "translator" for data, giving LLMs the ability to understand, organize, and make sense of raw data. In the world of data analytics, it helps make the process faster, more consistent, and easier to work with, which leads to better insights and smarter decision-making.
Unlocking the Power of LLMs
A 2023 technical report published results from a benchmark study showing that incorporating a semantic layer can dramatically improve the accuracy of LLM-generated responses. In the benchmark, using a semantic layer or knowledge graph increased the accuracy of GPT-4's responses from 16% to 54% when querying an SQL database. This threefold improvement highlights the semantic layer's crucial role in bridging the gap between raw data and its business context.
Another significant development is the semantic layer's ability to reduce the occurrence of hallucinations in LLM outputs. By enriching the LLM's training environment with deeply contextualized and well-tagged data, semantic layers help keep the model more grounded in reality. This improvement in output quality is crucial for businesses relying on AI-generated insights for critical decision-making.
The combination of semantic layers and LLMs is also revolutionizing self-service analytics. By joining the semantic layer's data translation capabilities with the LLM's language comprehension, businesses are creating powerful tools for customized and accurate data exploration. This synergy is making data analytics more accessible and user-friendly, allowing non-technical users to gain insights quickly and efficiently.
This is a big part of the work we are doing at Seek AI. Our system of LLMs is engineered for non-technical users to ask their data questions in plain English, and delegate the data work to our system of agents, which then interpret the words and recognize the most appropriate SQL code to suggest, but then also execute the query, summarize insights, and more.
As businesses continue to explore ways to leverage their data assets more effectively, the integration of semantic layers with LLMs is emerging as a key strategy for enhancing the accuracy, reliability, and usability of AI-powered analytics tools.
