Introduction
Large Language Models (LLMs) like GPT have become powerful tools for text generation, summarization, and reasoning. However, many real-world business problems rely heavily on tabular data — data organized in rows and columns, such as spreadsheets, databases, and CSV files. While LLMs are excellent at handling unstructured data like text, they need the right strategies and tools to work effectively with structured tabular data.
Why Combine Tabular Data with LLMs?
Most enterprises deal with huge amounts of structured data: sales reports, customer information, financial records, healthcare data, and more. Combining this structured data with LLMs allows:
Better Insights: Turn raw numbers into natural-language summaries.
Decision Support: Generate recommendations based on patterns in data.
Enhanced Automation: Create reports, answer queries, and support chatbots with real-time structured data.
User-Friendly Interaction: Make complex databases accessible via simple questions.
Example: A sales manager can ask, “Which product category had the highest sales in Q2?” The LLM can analyze the tabular data and provide the answer in plain English.