Translate the research results into industrial production processes

Translate the research results into industrial production processes — real work, not an imagined feature: named inside 14 evidenced career tasks. Below are four ready AI prompts for it, one per height of help: do it, make it easier to accept, decide when you are stuck, and change the pattern for good.

14career tasks name it
4prompt heights

The four heights

The same task, four distances: today's deadline, the next reviewer, the stuck moment, the pattern.

Execute — do the immediate task

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Translate last quarter's pilot reactor yields into an industrial process estimate for the…
Translate last quarter's pilot reactor yields into an industrial process estimate for the production team. Take the pilot run data, convert per‑kg reagent consumption and energy use to a 1‑ton output using the provided scale factors, calculate expected yield loss at scale using a 3% derating factor, and produce a clear sheet showing per‑ton reagent needs and a weekly throughput plan for the plant operations meeting on Wednesday.

Improve — make it easier to accept

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Before I give this to production, make the conversion clear to non-research staff. Put the per-ton…
Before I give this to production, make the conversion clear to non-research staff. Put the per-ton reagent and energy figures up top, explain where the 3% scale derate came from and how sensitive the total cost is to a 1% change, and flag the three assumptions that would make a production manager hesitate to commit to the schedule.

Decide — diagnose the stuck moment

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I applied the scale factors to convert pilot data to a one-ton basis and the reagent totals are far…

My scale factor produced hugely different reagent totals than last run.

I applied the scale factors to convert pilot data to a one-ton basis and the reagent totals are far off compared with the last conversion I did. The scale multipliers are the same numbers I used before, but some per-run entries look duplicated. I'm worried I accidentally scaled an already-scaled column. What checks will quickly reveal whether I double-scaled data, and how do I fix it while preserving an audit trail so production trusts the numbers?

Become — change the pattern

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We repeatedly waste time reconciling pilot-to-plant conversions because every engineer uses a…

Every scale-up we recreate conversions and lose trust with production.

We repeatedly waste time reconciling pilot-to-plant conversions because every engineer uses a different ad-hoc workbook and production questions the numbers. What workbook structure, naming conventions, and a minimal verification routine will stop repeated conversions and build credibility with operations?

Where the evidence lives

Who was seen doing this, and what people really ask.

Software tasks in the LLOS Work Atlas come from evidence, never a feature list: careers attested to do the work, real job descriptions, and the questions people actually ask (with their view counts). Facets — feature, workflow, troubleshoot, administer, deploy, scale — are open metadata: the work decides, not a taxonomy.
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The rest of the map

Same library, five ways in.