In today’s data-saturated business landscape, harnessing insights from overwhelming amounts of unstructured data is crucial, and Large Language Models (LLMs) have been at the forefront of this revolution.
LLMs shine when it comes to processing vast caches of text and extracting valuable insights. But when the stakes are higher—such as dealing with complex data or integrating multiple data types—LLMs often fall short. To truly unlock AI’s potential and drive business success, it’s time to think bigger.
This article delves into how a new class of AI called Compound AIs address the limitations of LLMs, providing businesses the depth, versatility, and precision that modern enterprises need to unlock more value from their data and stay ahead.
How LLMs Work
LLMs are a type of AI designed to understand and generate human language in a way that feels natural to actual humans. They learn by studying tons of text such as digitized books and websites, picking up on patterns between words, phrases, and sentences. When you ask them something, they predict what should come next based on what they’ve learned, which is why they’re great at chatting, writing, or summarizing.
LLMs get their superpowers by learning from tons of unstructured data—like what you find in books, articles, and social media. This messy, complex data is perfect for teaching these models to understand and generate text that feels natural and human-like. The more diverse the data, the smarter the LLM gets, picking up on patterns, grammar, and context.
This training is key to making LLMs flexible and powerful, allowing them to quickly probe unstructured data for valuable insights. For businesses, this means unlocking the potential of their data, whether it’s spotting trends in customer feedback or finding crucial information in documents. It’s like giving your data a voice that helps you make smarter decisions and stay ahead.
Investing in a clean data estate and strong data training is the secret sauce to making LLM-based AIs truly effective. Without these, LLMs wouldn’t be nearly as sharp or capable.
LLMs Struggle with Context and Error Correction
While LLMs perform exceptionally well in controlled environments, they can run into some challenges when put into production.
LLMs excel at recognizing patterns, but they don’t actually understand context like we do. This can make them less effective with more complex topics, mixing different types of data, or specific business contexts.
Further challenges include dealing with ambiguous prompts, navigating complex database schemas, and maintaining real-time performance. On top of that, integrating these models into existing systems can be a bit of a heavy lift, requiring ongoing effort to keep up with changing data and user needs.
Thus, outside of use cases that deal with simple question-and-answer interactions or surface-level knowledge, LLMs need help to really drive business value.
Introducing Compound AI: Bridging the Gaps in LLM Performance
To make AI even more powerful and useful, several different AI models can be combined, each good at different things. This is what we call Compound AI or AI Engines.
Think of it like building a super team, where each member has a special skill. One model might be awesome at understanding text, another might be great with numbers, and yet another could excel at recognizing images. By working together, these models can handle all sorts of tasks that a single model couldn’t manage on its own.
Additionally, individual AI models within a Compound AI system can check each other’s work, learning from mistakes, and fixing errors over time. This teamwork leads to better accuracy and reliability, as each AI contributes its unique strengths. By remembering and correcting errors, the system keeps getting smarter. In the end, this approach creates a stronger, more accurate AI system overall.
Compound AI Use Case: Text-to-SQL Queries
Take using AI for specific tasks, like accessing a database through text into SQL queries (text-to-SQL) in a production environment. A single LLM might struggle with handling a variety of queries, complex database schemas, and the real-time speed that a busy enterprise user demands.
For a text-to-SQL AI engine to be truly production-ready, it needs three key qualities:
- Understanding Diverse Queries: It should grasp the intent behind different user prompts and generate accurate SQL queries that fit the data model.
- Handling Ambiguous Schemas: It must navigate complex data models, clear up ambiguities, and avoid the errors that current LLMs can sometimes make.
- Minimizing Latency: It should deliver real-time responses, generating optimal queries quickly—ideally within the first few tries.
Now that we understand the needs of a text-to-SQL AI, we can build a compound AI system that attempts to meet these goals.
Optimizing AI with SherloQ: Skypoint’s Compound AI for Higher Accuracy
SherloQ, Skypoint AIP’s text-to-SQL engine, is designed to translate natural language queries into SQL with high accuracy. By combining multiple AI models, SherloQ overcomes the limitations of traditional LLMs. It excels in query translation, is capably of robust error handling, and seamlessly integrates with large-scale databases, making it ideal for complex, real-world data environments.
We designed SherloQ with a few key features to improve its performance and usability. SherloQ uses structured decomposition to break down queries, employs few-shot examples for improved SQL generation, and leverages data model context to ensure accurate domain-specific queries. It also incorporates a retry mechanism to handle errors and uses reflection to learn from past queries, continuously improving its accuracy and contextual awareness. A full overview of SherloQ’s capabilities can be found on our Hackernoon whitepaper: How to Build a Production-Grade Text2SQL Engine.
As a result, SherloQ is more reliable, versatile, and usable than out-of-the-box LLMs, and offers business a powerful tool for managing complex data.
SherloQ in the Real World: Finance Queries in Senior Living
Skypoint worked with a well-known senior living operator who was already using Skypoint AIP to analyze financial metrics across their facilities. In this scenario, our customer critically relied on accurate and reliable data.
Before SherloQ, this customer’s query accuracy was just 65%, and reliability was only at 60%. Not exactly what you’d hope for when making important financial decisions.
We ran a series of tests with SherloQ, running each prompt 100 times with unique identifiers to avoid any caching effects. Then, we measured SherloQ’s accuracy by comparing its responses to our benchmark, and checked its reliability by recording how consistently it gave the right answer.
As a result, SherloQ performed much better than the status quo, with 92% accuracy and 90% reliability. Because of this improved performance, our customer was able to interact with data more efficiently, leading to better and faster decision-making.
Make Better, Faster, and More Accurate Decisions with SherloQ
As AI continues to develop rapidly and the limitations of single LLMs become clearer, more and more AI use cases will likely depend on more complex and integrated AI systems. The shift towards Compound AI, where multiple models, agents, and AI systems collaborate, offers a more nuanced, scalable, and versatile approach to AI implementations across varied business contexts.
In highly regulated industries such as healthcare, insurance, and financial services, SherloQ’s improved accuracy and reliability over single LLMs makes it a powerful tool. If you’re looking to see how SherloQ can improve your business operations, book a demo with us today.
