What AI actually is

Outcome and preparation

Preparation: No prior AI experience, coding or account required. Use a notes editor or paper. No paid tools or real operational data.

What you will be able to do

• Explain the difference between a generated claim and supporting evidence.
• Compare two prompts against the same source facts.
• Repair a draft that invents a fact, then explain the correction.

Keep your bounded prompt, three-sentence proposal, claim table and repair explanation. Watching and review completion do not establish demonstrated capability.

Generation and evidence

AI includes systems for text, images, speech, prediction and other tasks. We begin with language models, which generate text from patterns learned during training and the context supplied for a request. They generate tokens: pieces of text that are not always whole words. You do not need to count tokens for today’s task.

A fluent draft is useful, but it is not proof. A model can reason about a task and still give an incorrect answer. A statement needs supporting evidence: a source, a calculation or an observed test that actually supports that statement.

A chat product may combine a model with search, tools, history or memory. Those features depend on the product and its configuration. A confident answer does not show that a source was retrieved. Ask what was available and what was actually checked.

For today, the useful question is simple: does this claim follow from the facts we supplied? Keep the distinction between producing a sentence and establishing that it is true.

Pause prompt: A draft says a trial saved 40%. What would establish that claim?

You would need evidence of the trial and its measurement, including what was compared. A polished sentence or a more capable model does not supply a trial that never happened.

Compare two prompts

Fictional source profile: managed 47 personnel and a $12 million fleet during a one-year deployment, with zero accidents and 96% mission completion. These are authored facts, not a real resume or hiring evidence.

Prompt 1: “Write a resume bullet for my logistics job.”

Prompt 2: “Using only the supplied fictional profile, write three active-voice bullets for a civilian supply-chain manager. Remove military jargon; use at most 25 words per bullet. Preserve all supplied figures and add no achievements.”

Here is a fictional logistics profile. Compare these prompts for the same task. The first says, “Write a resume bullet for my logistics job.” It does not tell the assistant which facts it may use, who will read the result, or how we will judge it.

The second supplies the profile, asks for three active-voice bullets for a civilian supply-chain manager, removes military jargon and limits each bullet to 25 words. Those are things we can inspect. The second prompt has a clearer factual basis and clearer constraints. This comparison is not a measured improvement in model performance or a promise about hiring.

Now write down three checks you would make on its output. Check whether every number matches the profile, whether the wording adds an achievement the source does not contain, and whether the requested audience and format are respected.

The repeatable loop is to ask with context, inspect against declared criteria, revise a specific failure, and recheck. Stop when the criteria pass, or when missing evidence prevents a defensible answer. Repeated drafting without new evidence does not turn an unsupported claim into a fact.

Prepare the worked case

Now we will do the whole process. Riley coordinates six people. The team handles 40 requests each week, and a request currently takes about 18 minutes. Riley wants to propose a two-week trial of AI-assisted request summaries. No trial has run, so savings are unknown.

The audience is a civilian operations manager. Our output is three sentences in plain language. It must preserve any facts we use, state that the trial is proposed, and avoid inventing achieved results.

Here is a bounded prompt: “Using only R1, write a three-sentence proposal for a civilian operations manager. Preserve the supplied facts, state that the two-week trial has not run, and do not claim savings. Propose a person checking each summary and measuring accuracy and processing time before an expansion decision.”

The checking and measurement steps are proposals. They are not statements that Riley has already done them. Put that distinction in the output.

Find the mistake

Our deliberately flawed draft says, “Our 20-person team has reduced processing time by 40% with AI. The system is ready for rollout across the organization. We recommend immediate adoption.”

Pause and mark what is wrong before opening the repair.

Repair and recheck

Here is one defensible repair: “Our six-person logistics team processes 40 requests each week, taking about 18 minutes per request. We propose a two-week trial of AI-assisted request summaries, with a person comparing each summary against the original request. We would measure accuracy and processing time before deciding whether to expand.”

Recheck the repair. The first sentence uses only the given workload facts. The second makes the trial duration explicit and labels the source comparison as proposed. The third describes a proposed measurement and decision, not achieved savings.

We have three sentences. We have not changed the numbers. We have not claimed success. A shorter repair could omit a figure; it cannot change that figure. This version is a supported proposal, not evidence that AI will save time.

Save your prompt, repair and claim table. Explain one edit: “I removed the 40% saving because no trial had run.” That explanation matters more than making your wording match mine.

Independent practice

Feedback after your attempt

Our answer is in the practice workspace. Write your attempt there and run the checks. Then “Compare with our answer” opens, and so does the full feedback here. Go to the workspace.

Transfer and next step

The skill you practiced is not tied to a logistics or community-center task. For another draft, identify the source facts, preserve what remains unknown, and distinguish drafting from permission to act. Ask, inspect, repair and recheck against clear criteria.

Keep your prompt, independent repair and fact-check note. Reviewing this lesson or watching the video does not certify the skill. Your artifact and explanation show what you actually did; an unfamiliar task will test whether you can repeat it.

The next lesson gives you five everyday transformations to practice. If you want the technical distinction between a model, product, harness and execution environment, use the optional reference. A harness supplies context, tool orchestration and controls; an execution environment determines where tools run and what they can reach. Those distinctions become important when a task needs tools, but you do not need to master them before repairing today’s claim.