Prompt variables are placeholders that insert row values into an AI prompt.
They let one prompt run across a whole spreadsheet.
For example:
Summarize this company in one sentence.
Company: {{Company Name}}
Website: {{Website}}
Description: {{Description}}
For each row, the variables are replaced with that row's values.
Why prompt variables matter
Without variables, you copy and paste data into a chat window. With variables, the prompt becomes a reusable workflow.
Prompt variables are useful for:
- Running ChatGPT on CSV rows
- Classifying text
- Scoring leads
- Translating product descriptions
- Extracting data from scraped text
- Researching one company per row
- Creating personalized email lines
They keep the prompt stable while the input changes.
Common prompt variable patterns
Use one variable when the task is simple:
Rewrite this product description for clarity:
{{Description}}
Use several variables when the answer depends on context:
Score this lead from 1 to 5 for a B2B SaaS outbound campaign.
Company: {{Company Name}}
Website: {{Domain}}
Industry: {{Industry}}
Employee count: {{Employees}}
Recent job post: {{Job Description}}
Use variables with instructions for missing values:
If {{Company Website}} is empty, return "Missing website" and do not guess.
Good variable names
Readable column names make prompts easier to maintain.
Prefer:
Company NameWebsiteJob TitleProduct DescriptionReview Text
Avoid unclear names such as col1, data, or misc.
📌 Keep prompts debuggable
If a result looks wrong, you should be able to read the prompt and understand which input field caused it.
Prompt variables in Datablist
Datablist uses variables such as {{Column Name}} in AI enrichments. You can map row data into prompts for Ask ChatGPT/OpenAI, Ask Claude AI, Ask Gemini, and other LLM providers.
Prompt variables also work in AI agent workflows when each row needs web research. For example, the agent can search for {{Company Name}} pricing page or read the website stored in {{Domain}}.
For more context, read LLM spreadsheet processing, batch LLM processing, and the guide on running ChatGPT on Excel and CSV rows.