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Sort and tag text in bulk using the Jev AI tool

You will learn how to use an AI tool called Jev to sort text into categories and apply tags. The guide explains how to process many items at once and set accurate rules using human-checked examples.

Original by OpenRouterClassification

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Credits

“Classify and Tag Text at Scale with Jev” by OpenRouter. Read the original source.

This expanded guide is an AI-narrated adaptation prepared by JevMade. It expands the source’s essential ideas, examples and caveats in JevMade’s own words and is not a word-for-word reading. The synthetic voice does not imitate the author or imply their endorsement.

Our summary

This guide explores how to organize large amounts of text, like social media posts, using an AI tool called Jev. Instead of writing new text, Jev chooses from a list of options. People use this to quickly group messages into main categories and apply multiple descriptive tags.

The method asks the AI to pick one main category for each message and rate how well other tags fit. To process thousands of messages without overloading the system, the software sends requests in small groups. Users then check a few examples themselves to decide the best cutoff scores.

This approach is useful for anyone needing to organize large piles of text on a budget. However, the specific cutoff scores found in the author's test on 550 tweets will not automatically work for your own data. You must always test your own examples to find the right settings.

Key takeaways

  1. Ask the AI to pick one main category and rate other tags independently.
  2. Find the right cutoff scores for tags by testing a small batch of human-checked examples.
  3. Control how fast the software sends requests so the system does not block you.

The author tested this method on a specific set of 550 tweets. The cutoff scores and costs from this test will not automatically apply to your own information.

OpenRouter cookbook · Source reviewed

Read the original guide Opens the author’s site in a new tab.