AI Prompting Basics and Common Scenario Templates
A Chinese prompt-engineering study pack covering task goals, context, constraints, examples, output formats and repeatable result evaluation.
Turn a vague request into a testable task
A useful prompt states the task, audience, input material, constraints, output format and success criteria. The goal is a repeatable experiment with visible assumptions, not a magic sentence that guarantees a correct answer.
Compare with fixed samples
Prepare two or three representative inputs, keep the model and evaluation criteria stable and compare accuracy, completeness, format adherence and human editing effort. Change one variable at a time and preserve failed examples.
Privacy and factual boundaries
Do not submit personal information, credentials or unapproved internal material to an external model. Fluent output may still contain outdated facts or invented citations, so important results need independent source checks.
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Prompt design and evaluation workflow
Define the task and success criteria, add context and constraints, compare fixed samples and keep a versioned record of outputs and corrections.
Before you start
- Use a model service or local model whose input and output capabilities are understood.
- Choose a practice task without personal, secret or internal confidential data.
- Prepare an experiment table for prompt version, model, parameters and quality results.
Quick start
- 01
Define the task
State the audience, deliverable, format and success criteria before writing the prompt.
- 02
Add context and limits
Provide necessary background, references, language, length, format and exclusions instead of asking the model to guess.
- 03
Compare small samples
Use two or three fixed inputs and compare accuracy, stability and readability across prompt variants.
- 04
Record and revise
Save the prompt, model, time and output, change one variable for the next round and keep the failure cases.
Usage tips
- Prompting does not replace fact checking or professional review for high-impact topics.
- Remove personal data, credentials and unapproved internal material before submission.
- Model, version, temperature and context changes can alter the same prompt's output.
Troubleshooting and uninstall
What if the output ignores the requested format?
Specify fields, order and a short example, remove conflicting requirements and test the same fixed input repeatedly.
What if the answer sounds fluent but facts are unreliable?
Ask for uncertainty and evidence, split the task into checkable parts and verify key facts with independent sources.
Frequently asked questions
What does a useful prompt usually contain?
It commonly includes the task, context, constraints, input, output format and evaluation criteria, adjusted to the task complexity.
Why does a template not guarantee correctness?
Output depends on data, context, model version and randomness; a template improves repeatability but does not replace verification.
Can real customer data be used for practice?
Remove personal information, credentials and confidential content first, and confirm the service's and organization's data rules.
How should two prompt versions be compared?
Use identical inputs and criteria, record accuracy, completeness, format adherence and the amount of human editing needed.