Ways to ground your AI in real data (so it stops guessing).
Your AI can answer business questions in seconds, but without your data it may guess. Grounding connects it to your actual rows and columns. Drop a CSV or TSV below to inspect what a grounded answer would read. To ask questions, open the separate chat tool and add your file there: chat with a CSV or TSV. The tool sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines.
New to the idea? Start with what grounding is and why it matters.
Drop a CSV or TSV to see the rows your AI should use
Your file contents are parsed in your browser and are not uploaded to Loam.
Why your AI guesses in the first place
Your AI predicts likely words. When it cannot read your business data, a confident-looking number may not come from your spreadsheet. That is why the wording can sound right while the figure is wrong. Grounding closes that gap by giving your AI the real values to calculate from. For a deeper look at how LLMs analyze data, grounding is the step that makes the rest trustworthy.
The main ways to ground an AI
Structured data grounding
Connect your AI to the rows and columns in your CSV or spreadsheet. Each number comes from the file, and you can see the query that calculated it.
Best for: Data analysis, financial reporting, metrics, and other questions where the numbers need to be exact.
This is how Loam grounds your AI. Drop a file above to inspect its rows. To ask your AI about the file, open chat with your data and add the CSV or TSV there. The tool sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines.
RAG (retrieval-augmented generation)
Retrieve relevant documents from a knowledge base and add them to the prompt, so your AI answers from the text it just read rather than from memory alone.
Best for: Text such as company policies, product documents, and support articles.
Limit for number questions: Your AI still reads numbers from prose. Exact calculations need a method that works from the source values.
Fine-tuning
Train your AI on examples so it learns your terminology and style. This helps with tone and vocabulary, but it does not give your AI the current numbers in your files.
Best for: Domain adaptation (medical, legal, or financial language).
Limit for number questions: Training examples do not give your AI the latest values in your files.
Knowledge graphs
Map relationships such as who reports to whom, which products belong to each category, and how things connect.
Best for: Relationship and taxonomy questions.
Limit for number questions: Knowledge graphs take ongoing work to maintain and are designed for relationships rather than numerical analysis.
Which one to reach for
Match the technique to the question. If the answer is a number from your tables, ground in the structured data. If it's knowledge spread across documents, use RAG. Most real setups combine them: RAG for the unstructured context, structured data grounding for the figures that have to be exact. The thing to avoid is leaving the AI to fill in numbers on its own. That is where hallucinated numbers come from.
Frequently asked questions
Ground your AI in your own numbers
The preview above stays on this page. Open the separate chat tool and add a CSV or TSV. It sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines.