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JevMade field notes / Architecture guide

Organizing task instructions so an AI assistant can find them easily

The author explains how to build a filing setup for task instructions. This helps an AI assistant search for the exact steps it needs to complete a job instead of reading through a massive list.

Original by NiazMorshed2007Agent workflows

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JevMade’s plain-English explanation

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Credits

“JCR · Jev Capability Resolver” by NiazMorshed2007. 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 project provides a way to organize saved task instructions into a strict step-by-step filing setup. Instead of handing an AI assistant a massive list of possible actions, this setup lets the software search for the exact steps it needs. This keeps the assistant from getting overwhelmed by extra text.

The setup uses Jev, an AI tool that chooses from options rather than writing an answer. When given a task, Jev narrows down the choices by picking categories step-by-step until it finds the right instructions. It scores different paths and returns only the most useful text back to the assistant.

This tool only looks up instructions and does not actually carry out the commands. The specific filing rules are designed just for this project, so developers will need to adapt them. Running the searches requires private access codes for the AI software, which will cost money to use.

Key takeaways

  1. Group saved instructions into categories so the AI assistant makes smaller, step-by-step choices.
  2. Give every category a clear name and description to help the search tool find the right path.
  3. Require every category to contain instructions and reject broken files before starting a search.

The filing rules described in this guide are specific to this project and are not a universal standard. In tests with twenty scenarios, this method reduced the amount of text the AI had to read, but it made one setup run slower.

Repository README · Source reviewed

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