๐Ÿ†“ FREE LESSON ยท Prompt 101
Lesson 2 ยท 8 min read

๐ŸŽจ 5 Prompting Patterns That Work Everywhere

These 5 techniques are used by all prompt engineering pros โ€” from Anthropic to OpenAI. Each is tailored to its own task type. Master them, and you'll cover 90% of business use cases.

1๏ธโƒฃ Zero-Shot โ€” A Simple Prompt Without Examples

The most basic form: you provide a task, and the model responds. No examples.

Example: "Translate to Spanish: Welcome to our store"

When to use: simple, straightforward tasks. Translation, summarization, obvious classification.

When it DOES NOT work: custom formats, specific style, edge cases.

2๏ธโƒฃ Few-Shot โ€” Providing Format Examples

You show 2-5 examples of the correct output, then ask a new question. The model copies the pattern.

Example:

Classify customer reviews into categories: POSITIVE / NEGATIVE / NEUTRAL.

Example 1: "Fast delivery, great product!" โ†’ POSITIVE
Example 2: "The order arrived broken, support is not responding" โ†’ NEGATIVE
Example 3: "Bought it, using it, nothing special" โ†’ NEUTRAL

Classify: "The price is fine, but the packaging is bad, the product itself is OK"
โ†’

When to use: when you need a specific response format. Classification, data extraction, custom formatting.

Pro tip: 3-5 examples is the sweet spot. Fewer and the model guesses; more and it loses focus.

3๏ธโƒฃ Chain-of-Thought โ€” "Thinking Out Loud"

You ask the model to reason step-by-step first, then provide the answer. The magic phrase is: "Think step-by-step."

Example:

I have a coffee shop in Kyiv. 50 orders/day, average ticket $8.
Rent expense is $1200/month, coffee+milk is $0.80/order, barista salary is $800.
What is my monthly profit, and is it worth expanding to a second location?

Think step-by-step, then provide a conclusion.

When to use: calculations, multi-step analysis, logic problems. Without CoT, the model often "guesses" the final answer and gets it wrong. With CoT, it reasons and hits the mark more often.

๐ŸŽฏ Fact: a Google study (2022) showed that Chain-of-Thought increases accuracy on complex tasks from 17.9% to 58.1%. This is the highest-impact pattern.

4๏ธโƒฃ Role-Play โ€” "You are an experienced X"

You assign an expert role to the model. It starts using the terminology, frameworks, and tone of that profession.

Example:

You are an experienced CFO with 15 years in SaaS companies. I am an early-stage founder,
my MRR is $8k, monthly churn is 6%, monthly growth rate is 12%.
Evaluate the health of my business using three metrics: LTV/CAC, magic number,
and Rule of 40. Give a recommendation: focus on growth or retention?

When to use: when you need an expert perspective. Finance, law, marketing strategy, code review.

Pro tip: combine it with CoT: "You are a CFO. Think step-by-step how a CFO would reason about this situation."

5๏ธโƒฃ Structured Output โ€” Fixed Format

Strictly define the output format. JSON, tables, Markdown with specific sections.

Example:

Analyze this email from a client: "[email text]"

Return strictly JSON in the format:
{
  "intent": "purchase | complaint | question | other",
  "urgency": "high | medium | low",
  "sentiment": "positive | neutral | negative",
  "suggested_action": "string"
}

No text before or after the JSON.

When to use: when your code / pipeline will parse the response programmatically. The model is great at generating JSON, CSV, or XML.

Pro tip: Claude and GPT-4 support "JSON mode" โ€” where it's virtually impossible to get non-JSON output.

๐Ÿฅ‹ Battle Combo of All 5

[Role]     You are an experienced sales analyst with a focus on B2B SaaS.
[Few-Shot] Examples of my successful emails:
            1. "{example 1}" โ†’ conversion 23%
            2. "{example 2}" โ†’ conversion 18%