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EDITOR’S LIST / ORDERED BY TASK STAGE

Read less. Choose what changes the next step.

Enter at the stage your work has reached. There is no popularity ranking and no requirement to read from top to bottom.

Choose an entry point ↓Browse by decision →
AI VISTA / READING FILE
EDITORIAL ORDER01—04
  1. 01Make the job concrete3 GUIDES
  2. 02Make output reviewable3 GUIDES
  3. 03Connect evidence and freshness2 GUIDES
  4. 04Control automated consequences4 GUIDES
ENTER WHERE YOU AREAI VISTA
01ONE DECISION FIRST
CURATED BY EDITORSNO POPULARITY WEIGHTONE JOB PER SET

LEARNING ROUTE / STAGE 04 · READING LIST

This stage fills the current decision gap without letting reading replace practice.

The editorial order follows four work stages: framing, review, evidence, and operations. Find the stage you are in, then read one or two guides that change the next action.

BRING
One unresolved decision from the course
DO HERE
Choose one or two guides by work stage
LEAVE WITH
A corrected decision or practical tool
WHEN THIS STAGE IS DONE Take the case to community Share the conditions, failure, or remaining question.

BEFORE YOU READ

Write three sentences before opening a guide.

Without a decision, an artifact, and evidence, another saved article usually becomes another unfinished task. Name all three so reading becomes a testable revision.

STOP RULEStop once one guide lets you edit the artifact and schedule the next test. Return only when a new failure reveals a new decision gap.
  1. 01
    DECISION TO CHANGE

    What decision am I willing to revise after reading?

    For example: whether support triage may write directly to the ticketing system.

  2. 02
    ARTIFACT TO EDIT

    Which piece of working material will this reading change?

    For example: failure handling in the task brief or the approval level in a permission ladder.

  3. 03
    EVIDENCE OF VALUE

    What observable change would make the reading worthwhile?

    For example: fewer misrouted cases in a ten-case pilot while risky cases still reach a person.

  4. →
    READY TO READ

    All three sentences point to the same real task.

    READY

A COMPLETE READING CHOICE

Not ‘this looks interesting,’ but ‘this can change the material on my desk.’

This example chooses one task-brief guide and stops as soon as it produces a testable revision. The list exists to shorten the distance between a blocker and the next practice—not to extend reading time.

Turn an AI idea into a testable task brief →
AI VISTA / READING TRACE01 GUIDE SELECTED
  1. 01
    CURRENT PROBLEM

    The team wants AI to handle the support inbox, but people disagree about which actions ‘handle’ includes.

    ↓
  2. 02
    WHY THIS GUIDE

    The gap is not another tool. User, input, acceptable output, boundary, and handoff are not in one working definition.

    ↓
  3. 03
    REVISION AFTER READING

    Replace ‘handle email’ with ‘suggest queue and urgency; send low-confidence and mixed-intent cases to the support lead,’ then complete one task brief.

    ↓
  4. 04
    WHEN TO STOP

    When two colleagues can use the brief to reach the same continue, revise, or stop judgment on ten cases, run the pilot instead of opening another guide.

    ■
STOP CONDITIONTHE ARTIFACT CAN CHANGE AND A TEST IS SCHEDULED
EDITORIAL INCLUSION & EXCLUSIONOPEN CRITERIA
  1. 01

    INCLUDE: it changes one concrete decision

    A guide states the task moment in which it helps and which working artifact should change afterward.

  2. 02

    INCLUDE: it has cases, an artifact, and a boundary

    The method survives at least one uncomfortable case and provides a usable tool plus conditions where it does not fit.

  3. 03

    EXCLUDE: concept-only, ranking, or generalized claims

    Popularity, unsupported effect numbers, generic model rankings, and tool inventories do not enter the editorial order.

01
FRAME

Make the job concrete

For turning an AI idea into testable work.

3 GUIDES
01 Should this task use AI at all?Judge a task by variation, verifiability, stakes, data boundaries, and fallback before choosing a model or building a workflow. Task framing 02 Turn an AI idea into a testable task briefReplace a vague AI feature request with a one-page brief that names the user, input, acceptable output, evidence, boundaries, and fallback. Task framing 03 Run a ten-case AI pilot before you scaleBuild a deliberately small test set with ordinary, difficult, ambiguous, and unsafe cases so a promising demo becomes evidence. Evaluation
02
REVIEW

Make output reviewable

For variable output and unclear failure causes.

3 GUIDES
01 Map the context before rewriting the promptSeparate instructions, source material, examples, history, tools, and output rules to see what the model actually receives. Context and prompts 02 Write the acceptance contract before the promptDefine required fields, evidence, tone, uncertainty, and rejection conditions before optimizing prompt wording. Context and prompts 03 Diagnose an AI answer before changing the modelTrace a bad answer to missing input, conflicting instructions, weak evidence, capability limits, or a broken handoff. Evaluation
03
GROUND

Connect evidence and freshness

For answers that require citations, access, and version control.

2 GUIDES
01 Decide whether RAG is worth buildingCompare knowledge volatility, source volume, citation needs, access rules, and maintenance cost before adding retrieval. Grounded answers 02 Test citations for support and freshnessAudit whether each important claim is supported by the cited passage, uses the right version, and survives a freshness check. Grounded answers
04
OPERATE

Control automated consequences

For agents about to use tools or change external state.

4 GUIDES
01 Give an agent the smallest useful permissionPlace each action on a five-level permission ladder, from read-only suggestions to explicitly approved irreversible work. Agent safety 02 Design recovery before the agent failsWrite the stop signal, saved state, owner, rollback action, and safe retry rule before automation reaches production. Agent safety 03 Choose a model with a job-specific scorecardWeight quality, latency, tool use, privacy, recovery, and operating constraints using cases from the workflow you will ship. Model operations 04 Map the full cost of an AI workflowCount input, output, retries, tools, waiting, review, and failure recovery instead of comparing a single token price. Model operations