SandboxHow to use AI and informationOrigin: Yolia's safe educational catalogExact route: /biblioteca?ruta=sandbox/guias/workspace-ia FULL PROFILEThe 17 points of this moduleEach section opens separately — tap the title to expand it. 1. Name and what screen it explains How to Use AI and Information. Sandbox guide that explains that here you can open prepared explanations about what Yolia does, a central topic in the complete catalog of Sandbox guides within Yolia's Library, part of the set of engines that process each case. 2. What this guide is within the Sandbox It is the guide that explains that here you can open prepared explanations about what Yolia does; educational AI should help you understand, not replace the missing data within the complete record of the case analyzed, within the complete process of analyzing a case. 3. What you should look at first on this screen Start with the big ideas and look at one thing at a time, using the clickable terms whenever a new word appears; if there is no measurement, an explanation can teach you what something means, but it does not turn it into a measured data point, inputs the system treats with their declared provenance. 4. Why it matters to understand this before moving on It is key because the explanatory layer separates knowledge, case evidence, inference, and response limits, with this guide being what connects several signals to avoid conclusions based on a single isolated case data point, a factor that shapes the quality of the final recommendation. 5. Easy explanation Here you can open prepared explanations about what Yolia does. The educational AI should help you understand, not replace the missing data, starting with the big ideas before getting into the detail of each explanation, designed for anyone looking for a general, accessible idea. 6. Medium explanation The explanatory layer separates knowledge, case evidence, inference, and the answer's limits; this screen serves to connect several signals and avoid conclusions from a single data point within the case being analyzed, for anyone who already knows the basics and wants to go deeper. 7. Advanced explanation The advanced reading adds physiological, chemical, or biological mechanisms and separates observed data from inference; it matters because a scientific decision must be mechanistically plausible, traceable, and proportional to the evidence, as the technical close of the three levels of explanation. 8. What Yolia observes at this step Yolia observes which data point comes first, what changes to another, and what evidence would be missing to be more confident, reviewing question, educational answer, data versus inference and Library link before answering the user, a risk the engine considers before letting a result through. 9. Related key concepts This guide's key concepts are inference, evidence, uncertainty and data quality, four ideas that help explain why an explanation doesn't magically turn something that wasn't actually measured into measured data, an important nuance for correctly interpreting this engine. 10. What other guide or engine it connects to It connects with the How to Use the Living Library Within the Case guide, which goes deeper into each specific concept, and with the Yolia Final Auditor, which verifies the coherence of the answers delivered within the case analyzed, a link that shows how the different engines coordinate. 11. Relevant differences between catalog species This guide's principle is identical for the catalog's 4 species; what changes is which specific explanations are relevant according to the particular species confirmed within the case currently being analyzed, a detail worth keeping in mind depending on the case being analyzed. 12. How this is practiced within the Sandbox It's practiced by reviewing question, educational answer, data versus inference and Library link on this same screen, with the option to open the next section once these explanations become clear to the user, an element visible when reviewing how Yolia reached its conclusion. 13. What happens if this screen is ignored If this distinction is ignored, the user could treat a general AI explanation as if it were an actual measured data point of the case, confusing educational information with concrete evidence obtained directly from the plant, a problem other engines couldn't correct on their own. 14. How Yolia uses it within the complete journey Yolia uses it to offer educational explanations that help understand the system's reasoning, always making clear which part of the answer is general knowledge and which part is case-specific evidence, a concrete application within Yolia's complete reasoning. 15. Common interpretation errors Thinking that an AI explanation replaces a missing measurement — an explanation can teach what a concept means, but it does not magically turn something that has not actually been measured into measured data, an interpretation error worth avoiding beforehand. 16. Evidence and limits of current knowledge Its specification recognizes that the quality of each answer depends on the quality of the evidence available in the case, with room for this explanatory layer to keep being refined within Yolia's complete system, with aspects that continue to be documented in more detail. 17. Related topics and guides How to Use the Living Library within the Case, related guide — Yolia Final Auditor, related engine — How to Read the Result, related guide within Yolia's complete Library catalog for cultivation, topics worth reviewing together within the catalog.