Dev
MongoDB Aggregation Prompt Generator
A MongoDB aggregation pipeline that filters late instead of early will scan every document before grouping — invisible on small datasets and catastrophic on large ones. This tool generates a fill-in prompt that tells an AI assistant where to put each stage and which index to suggest alongside the pipeline. Describe what you want to compute — for example, "monthly revenue per product category" — and name the collection. The prompt asks for a JS array of stages, a `$match` as early as possible to use an index, `$group` with the correct `_id` and accumulators, a `$project` keeping only needed fields, `$sort` with an optional `$limit`, an index recommendation, and a comment on each stage. Edit the goal and collection, paste the prompt into your assistant, and run `explain()` on the result before executing against a large production collection.
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Describe what you want the pipeline to compute.
- Enter the collection name.
- Click Generate to build the prompt.
- Paste it into your AI assistant and test the pipeline on sample data.
Use Cases
- •Getting a working aggregation pipeline from an AI assistant
- •Avoiding the slow pattern of filtering too late in a pipeline
- •Computing grouped metrics like revenue or counts per category
- •Standardising how a team requests MongoDB queries
- •Learning the order aggregation stages should run in
Tips
- →Name the fields you want grouped or summed for accuracy.
- →Ask for the index that supports the early $match.
- →Test with explain() before running on a large collection.
- →Project away fields you do not need to keep documents small.
FAQ
why does $match need to come first
A `$match` near the start of the pipeline can use an index, dramatically shrinking the document set before grouping and sorting run. Filtering late forces the engine to process every document first — far slower on large collections.
will the prompt ask for index recommendations
Yes. The prompt explicitly asks the assistant to note which index would make the pipeline efficient. That recommendation travels with the pipeline so you can create the index before running it in production.
is the generated pipeline ready to run as-is
It will be syntactically correct, but verify the field names against your actual schema and run `explain("executionStats")` on a sample to confirm it is using an index before executing against a full dataset.
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