Prompt Engineering: A Practical Guide

Prompt engineering is the craft of designing inputs that help AI models produce useful, reliable, and controllable outputs. This guide focuses on practical techniques, patterns, and checklists you can use immediately.

Plan

1. Why Prompt Engineering Matters

  • Clarity: Clear prompts reduce ambiguity and mistakes.
  • Control: Structure and constraints steer style, tone, and format.
  • Consistency: Templates make results repeatable across tasks and teams.

2. Core Principles

  1. Be explicit: State the goal, audience, format, and constraints.
  2. Give context: Supply examples, definitions, and edge cases.
  3. Constrain the output: Request bullet points, JSON, steps, limits, etc.
  4. Think step by step: Ask for reasoning, intermediate plans, or checklists.
  5. Iterate: Refine prompts based on what works; keep a versioned library.

3. Reusable Prompt Template (CRISPE)

CRISPE stands for Context, Role, Input, Steps, Parameters, Examples. Use it as a skeleton.

<CONTEXT>: [Brief background the model needs]
<ROLE>: You are a [role/persona] with [expertise].
<INPUT>: Here is the data/task: [paste data or describe task]
<STEPS>: Follow these steps: [numbered procedure]
<PARAMETERS>: Format, tone, limits: [e.g., JSON, 200 words, formal]
<EXAMPLES>: Positive example(s) and counterexample(s).

4. Common Patterns

4.1 Instruction + Constraints

Task: Summarize the article below for executives.
Constraints:
- 5 bullet points
- 1 risk and 1 opportunity
- Max 120 words
Text: """[paste]"""
Output: bullet list only.

4.2 Role Prompting

You are a senior Android engineer. Review this function for performance and readability.
Return:
- Issues
- Suggested diff
- Complexity (Low/Med/High)
Code:
```kotlin
// paste code
```

4.3 Chain-of-Thought Surrogate (Stepwise)

(Ask for steps or checks without exposing hidden reasoning.)

First list a plan. Then execute the plan. Finally, provide a brief QA check.
Topic: [topic]

4.4 Few-Shot Examples

Transform headlines to SEO slugs.

Example
Input: "Intro to GKE: Google Kubernetes Engine Overview"
Output: "intro-to-gke-google-kubernetes-engine-overview"

Now do the same for:

1. "[headline A]"
2. "[headline B]"

4.5 Output Schema (JSON)

Extract key fields as valid JSON only.

Text: """[paste]"""
Schema:
{
"title": "string",
"date": "YYYY-MM-DD",
"entities": ["string"],
"sentiment": "positive|neutral|negative"
}

4.6 Guardrails by Negative Examples

Write a neutral product description.
Avoid:
- Hype ("best ever", "revolutionary")
- Vague claims
- Pricing info

Input: [details]
Output: 80–120 words, neutral tone.

5. Anti-Patterns (Avoid These)

  • Vagueness: “Write something about X.” (What audience? length? format?)
  • Goal switching: Mixing tasks in one prompt without order.
  • Unbounded outputs: No length or format limits, causing rambling.
  • Hidden criteria: Expecting style or content that wasn’t specified.

6. Evaluation Checklist

  • Does the prompt state the goal, audience, and format?
  • Are there constraints (length, style, schema)?
  • Is there enough context or examples?
  • Is the acceptance criteria clear (what good looks like)?
  • Is the output verifiable (checklist, tests, or schema)?

7. Debugging Prompts

  1. Pinpoint the failure: Which requirement wasn’t met?
  2. Reduce ambiguity: Add examples and counterexamples.
  3. Change the format: Force JSON, bullets, or numbered steps.
  4. Break it down: Ask for a plan first, then the answer.
  5. Constrain knowledge: Provide the necessary facts in the prompt.

8. Domain-Specific Mini-Templates

8.1 Content Writing

Goal: Create a blog intro.
Audience: Cloud architects.
Tone: Professional, concise.
Constraints: 100–130 words, no fluff.
Topic: "Cost optimization on GCP"
Output: One paragraph.

8.2 Technical Explanation

Explain [concept] to a junior developer.
Return:
1) Analogy
2) Formal definition
3) Pitfalls (3 bullets)
Limit: 150–180 words.

8.3 Product Copy

Write a feature blurb.
Brand voice: Clear, credible.
Input: [feature details]
Output: 3 bullets:
- What it is
- Why it matters
- When to use it

8.4 Data Extraction

Extract fields into CSV rows (no prose).
Columns: name, email, company
Text: """[paste]"""
Output: code fence with CSV only.

9. System & Style Guards (Paste-In Snippet)

Follow these rules:
- Be concise and specific.
- If information is missing, state the assumption.
- Prefer lists and schemas over paragraphs when asked.
- Never invent data not provided.
- Respect length and format constraints strictly.

10. Maintaining a Prompt Library

  • Version prompts: Keep v1, v2, v3 with notes on changes.
  • Tag by use case: summarization, extraction, coding, QA.
  • Store examples: Save best inputs/outputs for reuse and training.
  • Document limits: When the prompt fails and why.

11. Quick Start: 3 Prompts You Can Use Today

Summarizer (Exec-Ready)

Summarize the text in exactly 5 bullets for executives.
Include: 1 risk, 1 opportunity.
Max 110 words.
Text: """[paste]"""
Output: bullets only.

Spec-to-Tasks

Turn this spec into an implementation plan.
Return:
- Milestones
- Tasks with owners
- Risks with mitigations
Constraints: 8–12 bullets.
Spec: """[paste]"""

Extractor (JSON)

Extract the fields as valid JSON only.
Fields: {"company": "", "use_case": "", "kpis": [""] }
Text: """[paste]"""

12. Conclusion

Strong prompts are clear, constrained, and context-rich. Treat them like product specs: define success, add examples, and iterate. With a small library of tested templates, you can boost quality, reduce rework, and scale your workflows.

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