AI Literacy── Eleven stages of working with AI, from a first spark to running it, in 10 series of 10 episodes
When AI does more of the work, what does the person directing it need to be good at? Drawing on the operator's own record of working with generative AI, this series names eleven stages, from a first spark and early conception, through locating the gap between ideal and as-is, finding words and keywords for vague unease, a rough wish, a concept, logic and blueprints, to programs and running them, and sets them out in 10 series of 10 episodes. One episode a day, over about three months.
7 of 100 published
S1Sparks and Conception7 / 10S2Ideal versus As-Is0 / 10S3Putting It into Words0 / 10S4From a Rough Wish to a Request0 / 10S5Concept0 / 10S6Making It Logical0 / 10S7Blueprints0 / 10S8Programs0 / 10S9Running It0 / 10S10AI Literacy0 / 10
How it is made The material is the operator's own record of requests, corrections and approvals with Claude since February 2026 (the operator has used generative AI since March 2023). Email addresses, server names, file locations, keys, personal and company names are removed; nothing is quoted; only recurring patterns are generalised. Every episode passes an anonymisation re-scan, a verbatim-quote check and a structure check before release.
S1 Sparks and Conception ── Turning a passing thought into work with AI
01
A Spark Can Go to AI as a Question
An idea need not become a finished request. Ask it as one sentence of goal and means, get four kinds of material and reasons against, and decide before you build.
S1-01Read →
02
The Three-Agent Team in the First Recorded Week
Splitting work by role gives each AI output a clear verification criterion. The three-role pattern from the operator's first recorded week shows why verifiability, not volume, should guide how work is handed to AI.
S1-02Read →
03
Start a Spark by Finding What Already Exists
Ask AI to search for existing solutions first. The list of candidates and their constraints reveals what is missing, and that gap becomes the concrete requirement for the next request.
S1-03Read →
04
Define What You Are Collecting Before AI Collects It
Before telling AI to collect information, have it define the target first. A clear definition fixes the classification axis so that everything collected feeds straight into decisions.
S1-04Read →
05
Let AI Inventory Your Own Expertise
Have AI read your past work and extract the structure of your expertise. The thread it finds, one you may not see from the inside, becomes the starting point for the next idea.
S1-05Read →
06
Reviewing Only What Is Written Will Not Reduce Errors
Reviewing only the text in front of you misses the gap between source and material. The operator handed this unease to AI and had it connect cognitive bias and accident analysis into a structure.
S1-06Read →
07
Redefine Reviewer Variance as Viewpoint Diversity
Redefine reviewer variance as viewpoint diversity, and AI can verbalize and integrate it, turning one person's review into collective intelligence. The shift in definition is the spark.
S1-07Read →
08
Ask AI for the idea that adds a dimension
When work feels flat, ask AI for viewpoints rather than answers.
S1-08In preparation
09
Ask 'what will it do, and what will it cost' early
Early value and cost estimates make it easier to drop weak ideas.
S1-09In preparation
10
Keep the record that feeds the next idea
Moving every AI conversation into a searchable second brain.
S1-10In preparation
S2 Ideal versus As-Is ── Stating the gap in a form AI can act on
01
Show 'it should work for any date' with examples
Pairing working and failing cases conveys the gap in one message.
S2-01In preparation
02
Restate the table you wanted as a table
When AI misreads, specify the deliverable's shape instead of explaining more.
S2-02In preparation
03
From a stale list to the hole in the system
Using a visible symptom to reach the cause: statically embedded lists.
S2-03In preparation
04
Turn one odd section into a consistency rule
Rephrasing a local oddity as a site-wide consistency requirement.
S2-04In preparation
05
Point to the missing day by its date
Asking by date lets AI trace the missing item to its cause.
S2-05In preparation
06
English pages need English content
Unless stated, AI translates the frame and leaves the content untranslated.
S2-06In preparation
07
Doubting AI's diagnosis revealed the real cause
Listing rejection reasons showed the search terms, not the supply, were wrong.
S2-07In preparation
08
Separate 'done' from 'verified'
Have AI split its report into verified and unverified parts.
S2-08In preparation
09
Paste the original request and ask if it was met
Checking the result against the literal request exposes design holes.
S2-09In preparation
10
Show the ideal by naming a model example
Point to a good example, and confirm which artefact is being redone.
S2-10In preparation
S3 Putting It into Words ── From vague unease to keywords and sentences
01
From 'make it cool' to 'tighten the letter spacing'
Turning an adjective into a measurable attribute makes it fixable.
S3-01In preparation
02
From 'too long' to an order of argument
Converting reader discomfort into words about argument order.
S3-02In preparation
03
It is fine to say 'I don't understand'
Ask AI to restate number-heavy reports in plain words.
S3-03In preparation
04
Symbols a newcomer cannot read fail
Judge private shorthand by the reader's standard and rewrite it.
S3-04In preparation
05
State the reader level as a number: 90 of 100
Give AI a numeric comprehension target instead of 'make it simple'.
S3-05In preparation
06
Have AI translate business needs for engineers
Rewriting a business request into a specification engineers can use.
S3-06In preparation
07
Define 'explain the essence, don't read aloud'
Turning unease about narration into a definition of tone and role.
S3-07In preparation
08
Ask what a word means before approving
Unchecked jargon leads to wrong approvals.
S3-08In preparation
09
Have AI state the essence of your own feedback
Turning feedback into a rule so it never has to be repeated.
S3-09In preparation
10
What the 14 vague-word requests turned into
A table of vague words in the record and the requests they became.
S3-10In preparation
S4 From a Rough Wish to a Request ── Turning what you roughly want into work AI can start
01
Queue ideas as reserved work
Registering new requests without interrupting the running task.
S4-01In preparation
02
Give the goal and let AI staff the team
Delegating team composition by stating goals and permissions only.
S4-02In preparation
03
Have AI write the hand-off note for another AI
Letting AI draft the brief another environment will execute.
S4-03In preparation
04
List the items before building
Agreeing on the item list before any design or code.
S4-04In preparation
05
Requests grew from 19 to 71 characters
Why terse commands became requests carrying definition-to-build order.
S4-05In preparation
06
Set the order yourself: 1, then 3, then 2
When offered options, choosing the sequence makes the plan safer.
S4-06In preparation
07
Japanese first, English after go-live
Stating a sequencing rule once, in a form that sticks.
S4-07In preparation
08
How to interrupt a running task
Separating the running task from the interrupting request.
S4-08In preparation
09
Make AI keep the request ledger and priorities
A ledger, a scoring rule and one foreground task keep requests in order.
S4-09In preparation
10
Put a three-month request on the ledger
Registering a long-term activity with period, cadence and milestones.
S4-10In preparation
S5 Concept ── Deciding in one sentence what to build
01
Have AI write the concept before building
Why the concept step is never skipped in classify-collect-design-review-build.
S5-01In preparation
02
Decide from the reader's moment: today's AI in 15 minutes
Defining a section by the reader's time and behaviour.
S5-02In preparation
03
One sentence on the screen before any algorithm
Separating the purpose sentence from the algorithm's assumptions.
S5-03In preparation
04
Fix the topic categories first
Early categories keep collection and writing consistent.
S5-04In preparation
05
A concept that learns from its own articles
Designing a writer that turns published work into its next prompt.
S5-05In preparation
06
Extract success factors from 54 sources
Turning common factors into a 100-point scoring rule.
S5-06In preparation
07
A design philosophy that evolves your old system
Redrawing an earlier algorithm at a higher level instead of replacing it.
S5-07In preparation
08
Revert a failed concept the same day
Trying a direction and withdrawing it when quality drops.
S5-08In preparation
09
Settling a name in three instructions
How the site name was fixed for English clarity and Japanese wording.
S5-09In preparation
10
Put the vision on one page and get approval
Show a large vision on one page and start only after approval.
S5-10In preparation
S6 Making It Logical ── Building with meaning, reasons and four questions
01
Write 'what it means' and 'why' separately
Separating meaning from reason turns a comparison into a decision aid.
S6-01In preparation
02
Argue from question to conclusion without showing the frame
Using the seven-step order while keeping its labels invisible.
S6-02In preparation
03
The screen carries facts, the voice carries meaning
The logic of never sending the same words to eye and ear.
S6-03In preparation
04
Hunt logic errors by 14 names
Naming fallacies keeps rewrites minimal.
S6-04In preparation
05
Define the unit before counting: one call
Cost estimates begin with what one call means.
S6-05In preparation
06
Turn a judgement system into a parts list
Norm hierarchy, verdict vocabulary, categories, machine-human split.
S6-06In preparation
07
'Important' only when two conditions overlap
Writing importance as the conjunction of two independent conditions.
S6-07In preparation
08
Drop per-chapter conclusions, remove repetition
Simplifying structure so no claim is made twice.
S6-08In preparation
09
Split roles between ML and LLM by logic
Prediction to ML, reading and critique to LLMs, confirmed by questions.
S6-09In preparation
10
Make argument rules countable by machine
Converting argument rules into countable checks.
S6-10In preparation
S7 Blueprints ── Components, flows and gates
01
Draw blueprints in three layers
Philosophy, then components, then execution flow.
S7-01In preparation
02
Build what is settled; don't wait for the rest
Splitting front and back so the settled part can proceed.
S7-02In preparation
03
Design the phone screen separately
Why device-specific design is requested separately.
S7-03In preparation
04
Measure 100 popular videos, then design
Using measured ranges from exemplars as design targets.
S7-04In preparation
05
Separate the slow step from the fast one
Decoupling generation from publishing and timing it to the inputs.
S7-05In preparation
06
Put gates in the flow: one failure, no release
Pre-release gates and post-release rollback belong in the design.
S7-06In preparation
07
Draw the off switch first
Kill switch and backups go on the blueprint before features.
S7-07In preparation
08
Feed the hand-made good version back into the flow
Automating the layout a person made by hand once.
S7-08In preparation
09
Design the routing between models
Routing judgement-heavy and volume-heavy work to different models.
S7-09In preparation
10
From logic to blueprint to runbook
Carrying a rule through logic, blueprint and manual into automation.
S7-10In preparation
S8 Programs ── Having AI build, run and verify
01
The first build: asking where each button is
Lessons from a first build done one settings screen at a time.
S8-01In preparation
02
Paste the error message as it is
Unedited error text leads to faster diagnosis.
S8-02In preparation
03
Free tier or your own server
Weighing free-tier sleep against server effort.
S8-03In preparation
04
A manual that lets someone else run it
Reproducibility from the manual as the definition of done.
S8-04In preparation
05
'Use this as the latest': rebuilding by version
Naming the version and diffing before every rebuild.
S8-05In preparation
06
Moving from the laptop to the server
What to check when moving a local job to an always-on host.
S8-06In preparation
07
Never paste keys into the chat
From pasting credentials to vaults and environment variables.
S8-07In preparation
08
Inspect before you install
Scanning third-party code for malware before installing it.
S8-08In preparation
09
Find where the lost work stopped
Recovering lost work by locating its last point in the logs.
S8-09In preparation
10
When the server stops answering, isolate by symptom
Panel, network, console: narrowing an outage down to disk failure.
S8-10In preparation
S9 Running It ── Automation, memory, ledgers and approvals
01
Publish at a fixed time every morning
Building daily auto-publishing from input and release times.
S9-01In preparation
02
Find Japanese-English mismatches every day
A standing consistency check with logged results.
S9-02In preparation
03
Turn the good run into the procedure
Recording a successful exchange and making it the standard procedure.
S9-03In preparation
04
Save it to memory and recall it next session
Storing an unbuilt plan in a ready-to-build state.
S9-04In preparation
05
Prevent duplication by mechanism
Turning a no-repeat rule into a check against past output.
S9-05In preparation
06
Approve briefly, once, in a batch
Batching approval questions and answering in a word.
S9-06In preparation
07
Never needing to say it three times
Repeated instructions become strict machine checks.
S9-07In preparation
08
Compare options by safety
Asking which option is safest and taking the uncompromising one.
S9-08In preparation
09
Every 'done' carries one line of evidence
Recording completion only after verification, with proof.
S9-09In preparation
10
Report progress as a number
Tracking a long activity by published/total and a weekly note.
S9-10In preparation
S10 AI Literacy ── Principles and practice
01
Check AI's reports against evidence
Demanding logs after a report of work that never ran.
S10-01In preparation
02
Ask for honesty when AI changes things unasked
Asking for the account, not an excuse, after an unrequested change.
S10-02In preparation
03
Evidence, not self-defence
Over-hedging reads as suspicious; stop at facts and grounds.
S10-03In preparation
04
Self-review by a similar AI is not independent
Why a sibling model's critique flattered itself, and how to get independence.
S10-04In preparation
05
Cite primary sources and always cross-check
Primary-source citations and number-by-number matching.
S10-05In preparation
06
Delete numbers you cannot confirm
Removing unconfirmable figures and measuring source reach.
S10-06In preparation
07
Search for sensitive data before publishing
Multiple checks for URLs, paths and keys before release.
S10-07In preparation
08
Look at every item
Checking all items, not a sample, reduces misses.
S10-08In preparation
09
Choose models knowing versions and billing
Understanding versions, prices and billing paths.
S10-09In preparation
10
Eleven stages and the human role
People decide, AI builds, mechanisms verify: the whole path in review.
S10-10In preparation