Templates, checklist, and glossary | Prompt Engineering Essentials

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Answer key and worked feedback

Use these explanations to diagnose your thinking. A correct letter without the right reason is only partial learning.

Module 1 answers

Module 2 answers

Module 3 answers

Module 4 answers

Module 5 answers

Module 6 answers

Module 7 answers

Module 8 answers

Final knowledge check answers

1. B Define what the model should do when dates are missing and forbid invented commitments.

2. A Open the authoritative source and confirm that it exists, is current, and supports the claim.

3. B External content is untrusted. Structure helps, but permissions, tool limits, validation, and review provide the real defense.

4. A Direct instructions, a clear end goal, and observable success criteria are broadly useful. Hidden chain-of-thought is neither necessary nor a substitute for evidence.

5. B OCR and vision can misread small text. Verify against the original and respect authorization and privacy.

6. B Draft -> exact preview -> confirmation -> action -> verification preserves human control.

7. A Representative cases and scoring criteria turn prompt improvement into evidence rather than anecdote.

8. SAMPLE A self-check asks the model to inspect its own work; independent verification compares important factual claims against a sufficiently separate authoritative source, calculation, tool, or qualified reviewer.

Prompt template library

Fast Start

TASK + CONTEXT + OUTPUT

Full Trust-First prompt

SEVEN-PART TEMPLATE

Prompt repair

DIAGNOSE -> CHANGE -> TEST

Source verification

VERIFY

Action preview

PREVIEW BEFORE ACTION

Action preview worksheet

Complete this before an AI-assisted send, publish, purchase, delete, access change, or other consequential action.

Exact target and scope:

Content or change to preview:

Permission or authority:

Cost and likely consequence:

Recovery or undo path:

Person who must confirm:

One-page Trust-First checklist

Use this before relying on an important AI output.

THINK

PROMPT

CHECK

IMPROVE

DECIDE

Glossary

Term	Plain-language meaning
Agent	An AI-enabled system that can plan or take actions using tools. Its real authority comes from the permissions and controls around it, not from the prompt alone.
Confabulation / hallucination	Plausible-sounding output that is false, unsupported, or invented.
Context	Information available to the model for the current task, such as instructions, conversation, documents, images, examples, or tool results.
Context window	The amount of information a model can consider in one interaction. A larger window can hold more material, but more material is not automatically more useful.
Few-shot prompting	Providing one or more examples to demonstrate the desired pattern or output.
Grounding	Tying an answer to supplied or retrieved evidence rather than relying only on general model knowledge.
Multimodal	Working with more than one type of input or output, such as text, images, audio, video, or documents.
Prompt	The instruction and context supplied to an AI system for a task.
Prompt engineering	The iterative practice of designing, testing, and improving prompts so outputs better meet defined requirements.
Prompt injection	Input that changes an AI system's behavior in an unintended way, including instructions hidden in external content.
Source boundary	A rule defining which evidence the model may use and what to do when the source does not answer the question.
Verification	Checking a important factual claim against suitable evidence or a reliable method sufficiently independent of the original generation.
Zero-shot prompting	Giving instructions without an example.