Define the Problem Before Writing the Prompt

Short answer

A strong prompt cannot repair a poorly defined problem; purpose, audience, constraints, evidence and success criteria must come first. The lesson connects four ideas—problem boundary, desired outcome, constraints and exclusions, and evaluation criteria—to one practical situation. Rather than treating these ideas as isolated definitions, the page shows how they work together. The learner first states the problem, then chooses evidence, performs a safe action and records what changed. For “Define the Problem Before Writing the Prompt”, this structure is useful beyond this topic because it makes reasoning transferable: the next unfamiliar tool or claim can be approached with the same disciplined sequence.

Why this matters

A strong prompt cannot repair a poorly defined problem; purpose, audience, constraints, evidence and success criteria must come first. For “Define the Problem Before Writing the Prompt”, this matters because a learner can follow a rule once without understanding when it applies, when it fails or how to recover from a mistake. Treat the first answer as a hypothesis to test, not a conclusion to defend. In the responsible ai context, the goal is not merely to remember vocabulary. The goal is to make a decision that another person can inspect, question and improve. For “Define the Problem Before Writing the Prompt”, an ai output is a proposal to inspect, not evidence by itself; responsibility remains with the people who define the task, supply data, test the result and decide how it is used. Good work keeps both the result and the route to the result visible. For “Define the Problem Before Writing the Prompt”, therefore every activity on this page asks for an artefact: a table, diagram, test record, checklist, explanation or short reflection.

Learning objectives

• Explain problem boundary and connect it to the main decision in the lesson.
• Use desired outcome to compare at least two possible actions.
• Create visible evidence by applying constraints and exclusions.
• Recognise the limits, risks or assumptions connected with evaluation criteria.

Four working principles

problem boundary is one of the central decision points in Define the Problem Before Writing the Prompt. For “Define the Problem Before Writing the Prompt”, responsible AI work makes the purpose, evidence, uncertainty, affected people and human decision point visible before an output is trusted or published. For “Define the Problem Before Writing the Prompt”, applied to the worked situation, this principle helps the learner decide what to inspect, which evidence to record and where a boundary should be placed. It also prevents the topic from becoming a list of rules with no reason behind them. For “Define the Problem Before Writing the Prompt”, the learner should be able to explain the principle in their own words, identify it in a new example and show one piece of evidence that the principle was actually used. In the case used on this page—a student asks an AI system to “make the best robot project” without defining age, materials, time or safety limits.—the principle changes the next action: instead of reacting immediately, the learner pauses, defines the relevant information and chooses a step that can be checked. A useful record includes the starting condition, the decision, the result and one limitation. That record becomes a learning artefact rather than a private impression.

The first useful lens is desired outcome . For “Define the Problem Before Writing the Prompt”, responsible AI work makes the purpose, evidence, uncertainty, affected people and human decision point visible before an output is trusted or published. For “Define the Problem Before Writing the Prompt”, applied to the worked situation, this principle helps the learner decide what to inspect, which evidence to record and where a boundary should be placed. It also prevents the topic from becoming a list of rules with no reason behind them. For “Define the Problem Before Writing the Prompt”, the learner should be able to explain the principle in their own words, identify it in a new example and show one piece of evidence that the principle was actually used. In the case used on this page—a student asks an AI system to “make the best robot project” without defining age, materials, time or safety limits.—the principle changes the next action: instead of reacting immediately, the learner pauses, defines the relevant information and chooses a step that can be checked. A useful record includes the starting condition, the decision, the result and one limitation. That record becomes a learning artefact rather than a private impression.

In this lesson, constraints and exclusions turns a broad idea into something observable. For “Define the Problem Before Writing the Prompt”, responsible AI work makes the purpose, evidence, uncertainty, affected people and human decision point visible before an output is trusted or published. For “Define the Problem Before Writing the Prompt”, applied to the worked situation, this principle helps the learner decide what to inspect, which evidence to record and where a boundary should be placed. It also prevents the topic from becoming a list of rules with no reason behind them. For “Define the Problem Before Writing the Prompt”, the learner should be able to explain the principle in their own words, identify it in a new example and show one piece of evidence that the principle was actually used. In the case used on this page—a student asks an AI system to “make the best robot project” without defining age, materials, time or safety limits.—the principle changes the next action: instead of reacting immediately, the learner pauses, defines the relevant information and chooses a step that can be checked. A useful record includes the starting condition, the decision, the result and one limitation. That record becomes a learning artefact rather than a private impression.

A reliable approach begins by making evaluation criteria explicit. For “Define the Problem Before Writing the Prompt”, responsible AI work makes the purpose, evidence, uncertainty, affected people and human decision point visible before an output is trusted or published. For “Define the Problem Before Writing the Prompt”, applied to the worked situation, this principle helps the learner decide what to inspect, which evidence to record and where a boundary should be placed. It also prevents the topic from becoming a list of rules with no reason behind them. For “Define the Problem Before Writing the Prompt”, the learner should be able to explain the principle in their own words, identify it in a new example and show one piece of evidence that the principle was actually used. In the case used on this page—a student asks an AI system to “make the best robot project” without defining age, materials, time or safety limits.—the principle changes the next action: instead of reacting immediately, the learner pauses, defines the relevant information and chooses a step that can be checked. A useful record includes the starting condition, the decision, the result and one limitation. That record becomes a learning artefact rather than a private impression.

Worked case

Situation: A student asks an AI system to “make the best robot project” without defining age, materials, time or safety limits.

The weak response would be to choose the fastest or most familiar action without checking assumptions. For “Define the Problem Before Writing the Prompt”, the stronger response begins by writing one sentence that defines the problem, one sentence that states what evidence would change the decision and one sentence that names a safety or privacy boundary. The learner then applies problem boundary before using desired outcome. After the action, constraints and exclusions is used to create a record, while evaluation criteria is used to review limitations.

A good case analysis does not pretend that every uncertainty disappears. It distinguishes a confirmed observation from an interpretation and a future question. For “Define the Problem Before Writing the Prompt”, that distinction is especially important for learners aged 10–15, because many digital, research and robotics situations look more certain on a screen than they really are.

A practical workflow

1. Write the exact goal in one sentence and remove words such as “best” or “safe” unless they are defined.
2. List what can be observed about problem boundary and what is still an assumption.
3. Choose one comparison or check based on desired outcome.
4. Perform the smallest safe action that produces evidence for constraints and exclusions.
5. Review the result through evaluation criteria and record at least one limitation.
6. Explain the final decision to another learner without hiding the evidence trail.

Practice lab

Practical task: write a problem brief first, then produce and compare two prompts derived from the same brief.

For Define the Problem Before Writing the Prompt, use a four-column page labelled starting condition, decision, evidence and next revision. The first column captures the situation before any change. The second states what you chose and why. The third contains an observable artefact rather than a claim such as “it worked”. The final column records what you would change if the same task were repeated.

Complete the activity once, then exchange the record with a classmate or trusted adult. For “Define the Problem Before Writing the Prompt”, ask them to identify which conclusion is strongly supported, which conclusion is only plausible and which detail is missing. Revise the record without adding private information or pretending that an untested step was completed.

Evidence and evaluation

For “Define the Problem Before Writing the Prompt”, evidence should be sufficient for the learning purpose but should not expose passwords, personal messages, precise locations, private photographs or information about another person. When the topic involves measurements, keep raw values as well as the final chart or average. When it involves research, keep the source path as well as the conclusion.

Common mistakes

• Using problem boundary as a label without showing how it changed the decision.
• Choosing one example for desired outcome and treating it as a universal rule.
• Recording only the final answer and losing the evidence created through constraints and exclusions.
• Ignoring the limits or recovery steps connected with evaluation criteria.

For “Define the Problem Before Writing the Prompt”, a useful correction is to return to the original goal, reduce the task and run one check that can disprove the current assumption.

Safety, privacy and limits

For “Define the Problem Before Writing the Prompt”, responsible AI work makes the purpose, evidence, uncertainty, affected people and human decision point visible before an output is trusted or published. For “Define the Problem Before Writing the Prompt”, use fictional or privacy-safe examples whenever real accounts, messages, images, locations or personal learning records could identify someone. Do not test security ideas on systems you do not own or have explicit permission to use. For “Define the Problem Before Writing the Prompt”, do not present a proposed project as Doruk’s completed personal work until real evidence and publication approval exist.

For mathematics and measurement tasks, use low-risk educational equipment and state units clearly. For research tasks, respect copyright and attribution. For “Define the Problem Before Writing the Prompt”, for study-system tasks, avoid turning a dashboard into surveillance: the purpose is reflection, not pressure or comparison with other children.

Lesson summary

Define the Problem Before Writing the Prompt can be summarised as a sequence: define the situation, apply problem boundary, compare through desired outcome, create evidence with constraints and exclusions, and review the result using evaluation criteria. For “Define the Problem Before Writing the Prompt”, the sequence is more important than a memorised slogan because it can be used again in an unfamiliar case.

The final learning goal is independence with boundaries. For “Define the Problem Before Writing the Prompt”, a learner should know what can be checked alone, what requires permission or adult support, and what must remain private. The work is complete only when the reasoning and evidence are clear enough to revisit later.

Review questions

1. What role does “problem boundary” play in Define the Problem Before Writing the Prompt?
2. What role does “desired outcome” play in Define the Problem Before Writing the Prompt?
3. What role does “constraints and exclusions” play in Define the Problem Before Writing the Prompt?
4. What role does “evaluation criteria” play in Define the Problem Before Writing the Prompt?
5. In Define the Problem Before Writing the Prompt, why is an evidence trail stronger than a confident conclusion?
6. In Define the Problem Before Writing the Prompt, what should happen when a result is uncertain?

Answers with explanations

1. What role does “problem boundary” play in Define the Problem Before Writing the Prompt?

   In Define the Problem Before Writing the Prompt, “problem boundary” gives the learner a specific lens for deciding what to inspect, compare or record. In the worked case it should change an observable action, not remain a vocabulary label.

2. What role does “desired outcome” play in Define the Problem Before Writing the Prompt?

   In Define the Problem Before Writing the Prompt, “desired outcome” gives the learner a specific lens for deciding what to inspect, compare or record. In the worked case it should change an observable action, not remain a vocabulary label.

3. What role does “constraints and exclusions” play in Define the Problem Before Writing the Prompt?

   In Define the Problem Before Writing the Prompt, “constraints and exclusions” gives the learner a specific lens for deciding what to inspect, compare or record. In the worked case it should change an observable action, not remain a vocabulary label.

4. What role does “evaluation criteria” play in Define the Problem Before Writing the Prompt?

   In Define the Problem Before Writing the Prompt, “evaluation criteria” gives the learner a specific lens for deciding what to inspect, compare or record. In the worked case it should change an observable action, not remain a vocabulary label.

5. In Define the Problem Before Writing the Prompt, why is an evidence trail stronger than a confident conclusion?

   For “Define the Problem Before Writing the Prompt”, because another person can inspect the observations, conditions and reasoning, identify a limitation and repeat or improve the work.

6. In Define the Problem Before Writing the Prompt, what should happen when a result is uncertain?

   For “Define the Problem Before Writing the Prompt”, the uncertainty should be labelled, the missing evidence should be named and the next safe check should be planned instead of presenting the result as proven.

Sources and verification note

The official or primary references listed below provide the technical and educational foundation for “Define the Problem Before Writing the Prompt”. These links support the concepts; they do not prove that a proposed project has been physically completed. Dates, software behaviour and policy details should be rechecked before future publication updates.

• UNESCO — AI Competency Framework for Students
• NIST — Artificial Intelligence Risk Management Framework 1.0
• NIST — Privacy Framework

Next step

For “Define the Problem Before Writing the Prompt”, return to the module page, complete the evidence artefact for this lesson and continue to the next item in sequence. For “Define the Problem Before Writing the Prompt”, a project should be presented as completed personal work only after real testing evidence and publication approval exist.