Gen BI vs. Gen AI: Which Should You Be Using?

what's going on behind the scenes when you toss a question into a business intelligence tool? Well, today we're going to look at how Python's LLM and Pyramid's LLM actually work with your queries. Think of it as a peek behind the curtain to see how these systems turn your data puzzles into clear insights. Whether you're a data analyst or just getting started, understanding this can make a big difference.

Ready to see how it all works?

The Python-Based LLM Approach:

• User Query Entry: Initiates with the user entering a query via a chat interface.

• LLM Processing: A Large Language Model (LLM) interprets the query to craft a bespoke Python script.

• Script Crafting: The Python script is authored to encompass the entire sequence of extracting data, executing analytical operations, and generating visual results

Execution Complications:

The Pyramid LLM Approach:

• Diverse Input Methods: Users can initiate queries not only through typing but also via speech recognition, showcasing input flexibility.

• Pre-Processing Layer: The system pre-empts the process by understanding the semantics and relationships of data sources, which is crucial for database connections.

• LLM Recipe Generation: An LLM processes the request to yield a compact 'recipe', eliminating the need for verbose scripting required in the Gen BI approach.

• Pyrana & Visual Engines Utilisation: Pyramid utilises its internal engines, such as 'Pyrana' and visualisation engines, to action the recipe, carrying out data queries, analysis, and crafting interactive outputs.

• Interactive and Iterative Results: The outputs are not only interactive, allowing further user exploration, but they are also dynamic, adapting to subsequent user queries in real-time.

• Streamlined Iteration: This architecture supports an unbroken cycle of questioning, where users can pose follow-up queries and the system dynamically adapts, all without manual coding or interruption.

The architectural flow demonstrates that while the Python LLM approach provides a structured pathway for data analysis through scripting, it is less flexible and more static. In contrast, the LLM method with Pyramid's ecosystem offers a more agile, user-centric experience with interactive and iterative capabilities, making it a favourable choice for dynamic business intelligence needs.

Tigma Technologies, Inc. gearing up to introduce Pyramid's Gen BI tools into our workflow. This isn’t just about stepping up our tech game; it’s about transforming the way we analyze data to make smarter, faster decisions that keep us ahead of the curve.

Why Pyramid’s Gen BI, you ask? Well, it’s all about getting real-time insights that can pivot as quickly as the market does. With these tools, we can expect our data analytics to be more interactive and responsive, enabling us to adapt strategies on the fly and really hone in on what our customers need.

A huge thanks to Avi Perez , Co-founder & CTO at Pyramid Analytics , for his invaluable insights. He has walked us through how these tools can directly benefit our daily operations and long-term goals.