The Scenario
You are a product manager and you have 12 plain-English research objectives sitting in a Google Sheet — things you need to investigate before the next planning cycle. The objectives are written the way you think: "find all open-source LLM frameworks with more than 5,000 GitHub stars," "identify the top 5 enterprise knowledge management tools launched in the last 18 months," that kind of thing. The planning cycle starts in a week and you need the research done, not just queued.
The bad version:
- Read the first objective, figure out what API call or search query would answer it, write that query, run it, interpret the output, and write a summary into the adjacent column.
- Realize that some objectives require a different Parallel endpoint than others, spend time consulting the API docs, adjust your approach per objective.
- Get through six of the twelve before the workday ends, with six more still untouched.
The objectives did not write themselves — that was the thinking. Executing them should not take as long as formulating them did.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads your research objectives and through its built-in Parallel integration generates a structured task spec for each one, executes the task, and writes the task ID and result summary back into the sheet.
For each research objective in column A of this sheet, use Parallel to generate a structured task specification and then execute it. Write the Parallel task ID into column B and a one-paragraph result summary into column C for each row.
What You Get
- Column B filled with the Parallel task ID for each executed research task — traceable and referenceable.
- Column C containing a one-paragraph result summary per objective, written in plain language.
- The task specs generated automatically from the natural-language objectives, without you having to translate them into API syntax.
- Any objective that generated a task but returned an empty result noted in column D so you know what needs a follow-up prompt.
What If the Data Is Not Quite Ready
Some objectives are too vague to generate a useful task spec
For each objective in column A, evaluate whether the objective is specific enough to generate a meaningful Parallel task spec. If an objective is too vague (e.g., "research AI tools"), write Needs Refinement into column D and skip execution for that row. For all specific objectives, generate the task spec, execute it, and write the task ID into column B and result summary into column C.
You want to see the generated task spec before execution, not just the result
For each research objective in column A, use Parallel to generate the task specification but do not execute it yet. Write the full task specification as a JSON-formatted string into column B. Then, in a second pass, execute all tasks where column C is blank, writing the Parallel task ID into column C and result summary into column D.
Objectives reference different data sources — some need web research, others need document extraction
For each objective in column A, classify whether the task requires web research or document extraction based on the objective language. Write the classification (Web Research or Document Extraction) into column D. Then generate the appropriate Parallel task spec for each type and execute it. Write the task ID into column B and result summary into column C.
You want task spec generation, execution, result summarization, and follow-up question generation in one shot
For each objective in column A, use Parallel to generate a task spec, execute it, and write the task ID into column B and a one-paragraph result summary into column C. Based on the result summary in column C, generate one follow-up research question that the result raises but does not answer and write it into column D. For any row where the result was empty, write a revised version of the original objective that is more specific into column E.
The pattern: objective-to-result plus follow-up generation in one prompt — so the research cycle moves forward rather than stops at the first pass.
Try It
Get the 7-day free trial of SheetXAI and open any Google Sheet with a column of plain-English research objectives, then ask it to generate Parallel task specs, execute them, and write the results back into the sheet. You can also look at how to run Parallel task groups or return to the Parallel overview.