AI doesn't know about your school.

Ask Claude: what is “Mars Pro”? You'll get an answer about a planet or some software. Why doesn't AI know about your school? Because Mars IT School is niche/local. In this module — how to describe a task so that AI doesn't guess, and how to understand where it's guessing by default.

Hook — “Mars Pro”? AI thinks about a planet.AI o'qituvchining maktabini bilmaydi

Mental model — what AI knows and how to tell it.AI nimani biladi va unga qanday tushuntirish

There are two layers here. Knowledge — what the model already has in its head from training. Description — how you package your task to make it retrieve and apply that knowledge. Let's break down both.

1. What AI knows well, and what it will invent

The practical question isn't "does AI know this?", but "how well was this represented in what it read?". A four-zone scale:

2. Three Levels of Description — product · process · performance

3. Description ↔ Discernment loop — the course engine

Antipattern №1 in Mars: mentor writes one long prompt → gets the wrong thing → blames the model. Pattern: short prompt → you see what the AI did → you understand which layer was missing → you add it. After 2-3 rounds — a working template that you can then reuse.

Six Techniques prompting — from Anthropic Fluency.Oltita texnika — prompting uchun

Six techniques, each fitting into product / process / performance. Remember these six, and they'll cover 90% of your prompts.

Anthropic calls it a “secret weapon”: “Ask the AI itself to help improve your prompt.” Finished your prompt? Ask Claude: “What in this request is unclear to you?” Often a whole layer you hadn't considered pops out from there.

Demo — four iterations of one prompt.Bitta prompt — to'rtta variant

Zuhra, a Python mentor from Yunusobod. Monday morning, she needs to prepare material for a group of 12-year-olds. The same basic request. Each version adds one Description layer. Mock-output shows what changes in quality.

V1 · bare — no layers

$ claude

› come up with 5 Python assignments for 12-year-olds

⠋ Done:

1. Sum of squares from 1 to n
2. Check if a number is prime
3. FizzBuzz
4. Guess the number (random)
5. Count vowels in a string

What's wrong: technically valid, but it's a generic Hour-of-Code set. For which 12-year-olds exactly? Just started? Already know conditions? What's the topic — loops, strings, dictionaries? It's completely unclear how to evaluate these. Discernment signal: the prompt hit mainstream AI knowledge and pulled the most average from the training data.

V2 · +product — what I want as output

› come up with 5 Python assignments, topic for/while,
  for 12-year-olds, who already know if/else but have just started loops.
  Format: 1 sentence condition + 1 example input/output.

⠋ Done:

1. Print numbers from 1 to 10, each on a new line.
   Example: input — none, output — 1\n2\n...\n10
2. Calculate the sum of all even numbers from 1 to 20.
   Example: input — none, output — 110
3. Print the multiplication table for 5 (up to 5×10).
4. Until the user types "stop", repeat the greeting.
5. Find the first number starting from 1 whose square is greater than 100.

Better: now on topic (loops), now considers the level (they know if/else). But it's still abstract, like school. "Sum of evens" for a 12-year-old — why? What's a typical mistake? What's the pedagogical approach? The AI doesn't know because we haven't told it how to think.

V3 · +process — how to think

› the same 5 assignments on for/while for 12-year-olds (they know if/else).
  For each assignment, think in steps:
    1. concept (what exactly from loops are we mastering)
    2. real-life example for a child (not "sum of evens" — something about
       their world: WhatsApp, TikTok, homework, recess)
    3. most common expected mistake (forgot increment? off-by-one?)
    4. assignment

⠋ Done:

1. CONCEPT: for with a range
   REAL-LIFE: scrolling through a TikTok playlist — each video is 15 seconds
   MISTAKE: they'll forget that range(5) gives 0..4, not 1..5
   ASSIGNMENT: "Print 'video N (15 sec)' for 8 videos."

2. CONCEPT: while with an exit condition
   REAL-LIFE: waiting for a friend's reply on WhatsApp — until it arrives
   MISTAKE: infinite loop — they'll forget to change the variable
   ASSIGNMENT: "Until you get 'ok' — keep printing
                'waiting...' with a pause." (show time.sleep)

3-5. [similarly — push-up counter, recess countdown,
        checking if all friends have replied]

Much better: The AI started giving pedagogically meaningful material. "Expected mistake" is almost a lesson plan. But we still haven't told it how you'll know if an assignment is strong. What if the AI gives a weak "real-life example"? It has no criteria to discard it.

V4 · +performance — how you'll know what's good

› [V3 prompt completely] +
  CRITERION FOR A GOOD ASSIGNMENT:
  "A 12-year-old child can retell the assignment condition
   in their own words in 30 seconds." If an assignment requires
   explaining the concept longer — it's weak, discard it.

  First, create 8 candidates, then discard
  them based on this criterion, return the 5 final ones
  + show which 3 you discarded and why.

⠋ Thinking…
⠋ Generated 8 candidates.
⠋ Running each through the 30-second test…

DISCARDED 3:
  × "Caesar cipher using a loop" — requires explaining encryption,
     30 seconds isn't enough, it's a loaded concept for a 12-year-old
  × "Bubble sort" — the concept of "sorting with an algorithm"
     itself requires explanation, plus while inside for
  × "Prime numbers" — need to explain primality, 30+ seconds

5 FINAL (passed 30-sec test):
1. TikTok Playlist [as in V3]
2. WhatsApp Wait [as in V3]
3. Push-up Counter [as in V3]
4. Countdown to End of Break [new — better than push-ups]
5. Group Chat Friend Check [new]

The main thing that happened: The AI started to filter out weak options itself. You didn't have to argue with it on every task. The performance criterion did the work inside the AI that a mentor's eyes would do without it.

Now multiply that by Mars context. Zukhra's ~/zukhra/CLAUDE.md (from M1) says: "I'm a Python mentor for 11-13 year olds at Mars IT School, my kids are from Yunusobod, native Uzbek, Russian as a second language, instructions and examples in both languages, 70% have basic Scratch". This file is loaded automatically. V4 with Mars context no longer just gives 5 tasks — it gives them bilingually, with a note "these 2 assume the child has seen Scratch blocks — for the 30% who haven't, explain in 2 minutes". What would have taken a V5 iteration without CLAUDE.md, comes immediately with CLAUDE.md.

Edge of knowledge — where AI honestly doesn't know

Zuhra tries: "explain recursion using an Uzbek folk tale — for 12-year-olds". The AI cheerfully responds with "like Ivan Tsarevich..." — Uzbek tales are rare/local in its training. If not stopped, the material will go to children with Russian names.

Three places to "put" your context:

Exercise — 3 probes and a template-snippet.Uchta probe va shablon

Three exercises on real tasks. Do all three manually — without this, your next prompt will be a guessing game, like everyone else's.

Quiz — check yourself in 3 minutes.O'zingni sina — 3 daqiqada

Three apply-questions. If you made a mistake in at least one, return to the corresponding section. Not a grade, but an insurance before the next prompt.

What's next — Module 4.Keyingisi — Modul 4