Best Practices for Prompt Engineering: A Practical Guide

*Note: This article was originally written on Medium. I don't write there anymore.*

Prompt engineering is a craft: clear goals, careful constraints, and iterative refinement turn ordinary prompts into reliable, repeatable instructions. Below are ten practical best practices used by practitioners and engineers when designing prompts for large language models. Each section includes a short explanation and a concrete example you can copy, paste, and adapt.

1. Start simple and refine through iteration

Why: Begin with the smallest instruction that produces an acceptable result. A minimal prompt reveals the model’s baseline behavior; subsequent iterations add constraints to correct errors.

Example — Minimal

Prompt:

Summarize the following paragraph in one sentence:
"[input paragraph here]"

Example — Improved

Prompt:

You are an editor. Summarize the following paragraph in one sentence that
emphasizes the main claim and outcome. Do not exceed 20 words.
"[input paragraph here]"

2. State instructions clearly and explicitly

Why: Defining a persona and an objective reduces ambiguity about style, depth, and assumptions the model should adopt.

Example

Prompt:

You are an experienced data scientist.
Explain the difference between precision and recall to a junior engineer
in three bullet points, each with a short example.

Expected output:

Three bullets: concise definition + one-line example for each metric.

3. Be specific and detailed in your requirements

Why: Vague instructions lead to inconsistent outputs and hallucinations. State length limits, structure, tone, and audience level to ensure repeatability.

Example

Prompt:

Write a formal email (150–200 words) to the product manager requesting
a one-week deployment delay. Include two concrete risks and one mitigation
strategy. Use a professional tone.

4. Provide examples or templates for the desired output

Why: When you require a specific structure (JSON, table, checklist), give a template or one to three examples so the model reproduces the schema exactly.

Example

Prompt:

Produce a JSON object with keys: "title", "summary", "tags".
Example:
{"title":"...", "summary":"...", "tags":["tag1","tag2"]}
Now generate JSON for the attached article text: [article text]

Expected output:

One valid JSON object matching the schema.

5. Avoid negative instructions. Improve the quality of your instruction instead

Why: Telling the model what to do is more reliable than telling it what not to do. If you must forbid something, restate the desired alternative.

Less effective

Prompt:

Don't use buzzwords or clichés in this product description.

More effective

Prompt:

Write a product description using concrete features and measurable benefits.
Use plain language; avoid phrases such as "game-changer" or "world-class."

6. Progress from simple to complex (zero-shot to few-shot)

Why: Start with zero-shot to observe default behavior. If the task is complex or requires a particular style, supply one to five examples (few-shot) that the model should emulate.

Example — Zero-shot

Prompt:

Convert the following bullet list into a short persuasive paragraph:
[bullets]

Example — Few-shot

Prompt:

Example 1:
Bullets: [A]
Output: [rewritten paragraph A]

Example 2:
Bullets: [B]
Output: [rewritten paragraph B]

Now convert: [new bullets]

7. Reduce unnecessary fluff and keep prompts concise

Why: Extra, irrelevant information increases the chance of contradictions and hallucinations. Provide only what the model needs to complete the task.

Inefficient

Prompt:

I want you to act like someone who’s read dozens of textbooks on marketing.
Write something persuasive but not too long and avoid being salesy.

Efficient

Prompt:

Write two persuasive lines promoting a time-management app.
Tone: professional, restrained. Avoid superlatives.

8. Use leading cues and structure for code generation

Why: For code generation, state language, function signature, input/output types, and testing expectations. Leading verbs such as “Implement”, “Return”, and “Function signature” reduce ambiguity.

Example

Prompt:

Implement a Python function
def merge_intervals(intervals: List[List[int]]) -> List[List[int]]:
that merges overlapping intervals.

Provide only the function definition and a short docstring.
Include one doctest example.

Expected output:

A single function implementation with a docstring and a doctest.

9. Define clear acceptance criteria and constraints

Why: Specify measurable success conditions (word limits, schema validation, performance constraints). This enables automated checks and reduces iteration.

Example

Prompt:

Summarize this research paper in at most 120 words. Include:
(1) research question, (2) method, (3) key result.
Do not include citations or verbatim quotes.

Expected output:

A ≤120-word summary with three labeled sentences: Research question, Method, Result.

10. Establish voice, audience, and contextual assumptions

Why: Consistent voice and assumptions yield consistent outputs. State the target audience and any prior knowledge the model should assume.

Example

Prompt:

You are a senior backend engineer writing for mid-level engineers.
Explain how to design an idempotent payment endpoint in five short steps.
Tone: concise and technical.

Quick checklist for production prompts

Workflow tips for reliability

Closing note

Effective prompts are engineered, not improvised. The more you iterate, test, and refine, the more predictable your AI integration becomes.