🔧 Improving the Capabilities of LLM-Based Analytics Copilots With Semantic Search and Fine-Tuning

Picture this: You're an analyst drowning in a sea of data, trying to make sense of complex attribution models and customer journeys. Wouldn't it be great if you had a super-smart AI assistant that could instantly answer your questions, generate SQL queries on the fly, and break down complex tabular data? Well, that's exactly what we're working on with Large Language Model (LLM)- based analytics copilots. But as with any cutting-edge tech, it's not all smooth sailing. Let's dive into the challenges we faced and the cool solutions we came up with to make these AI assistants truly shine.

The LLM Conundrum: Brilliant, but Flawed

First things first: let's talk about why we're so excited about using LLMs in analytics. These language models are like the Swiss Army knives of the AI world – they can tackle a wide range of tasks, from answering questions to generating code. For us analysts, that means: