engineer-prompts-for-instant

name	engineer-prompts-for-instant
description	Guide to writing stable prompts for instant/fast models (Gemini Flash, GPT-4o Mini, Haiku). Use when the user is writing or tuning prompts for a fast or cheap model.

HOW TO WRITE PROMPTS FOR INSTANT MODELS

This guide helps you get stable, accurate results from high-speed models (like Gemini Flash, GPT-4o Mini, Claude Haiku). These models are fast and cost-effective but need clear instructions and examples to work best.

1. THE GOLDEN RULE: "SHOW, DON'T JUST TELL"

Instant models are like fast learners who prefer copying a pattern over reading a textbook. The most effective way to improve their performance is to provide examples.

• Don't just say: "Extract the dates."
• Say: "Extract dates in YYYY-MM-DD format. Example: 'March 5th' -> '2025-03-05'."

2. THE 4-PART FORMULA (BEGINNER TEMPLATE)

Use this structure for 90% of your tasks. It is designed to be copy-paste friendly.

# 1. ROLE (Who matches the task?)

You are an expert [Role Name, e.g., Data Analyst, Copy Editor].

# 2. TASK (What to do?)

[Action Verb] the [Input Data] to produce [Result].

- Be direct. Use commands like "Extract", "Summarize", "Translate".

# 3. RULES & FORMAT (How to do it?)

- Output format: [JSON, Markdown Table, Plain Text, etc.]
- Constraint 1: [e.g., Do not include markdown code blocks]
- Constraint 2: [e.g., If data is missing, write "N/A"]

# 4. FEW-SHOT EXAMPLES (CRITICAL FOR STABILITY)

Input: [Short Example Input] Output: [Perfect Example Output]

Input: [Short Example Input 2] Output: [Perfect Example Output 2]

# ACTUAL INPUT

[Paste your real data here]

3. KEY TECHNIQUES FOR BEGINNERS

A. Few-Shot Prompting (The "Examples" Section)

Always give at least one example (1-shot), ideally three (3-shot). This fixes formatting errors better than any written instruction.

B. Chain-of-Thought Lite (Thinking Tags)

Even fast models can make mistakes on math or logic. Ask them to "think" before answering.

• Instruction: "Think step-by-step in <thinking> tags before outputting the JSON."
• Why: This gives the model "space" to calculate before committing to an answer.

C. Negative Constraints

Tell the model what NOT to do.

• "Do not add introductory text."
• "Do not explain your reasoning, just give the code."

4. TROUBLESHOOTING COMMON ISSUES

Problem	Solution
Model ignores format	Move the "Output Format" section to the very bottom, right before the Input.
Model hallucinates details	Add a rule: "If the answer is not in the text, state 'Unknown'."
Response is too chatty	Add: "Return ONLY the result. No conversational filler."
Logic is flawed	Ask the model to output a <thinking>Step 1... Step 2...</thinking> block first.

5. EXAMPLE: TEXT EXTRACTION

Task: Extract meeting items from a rough email.

# ROLE

You are a personal assistant.

# TASK

Extract action items from the email.

# RULES

- Output a JSON list of strings.
- Only include tasks with a deadline.

# EXAMPLES

Input: "Hi, can you buy milk by 5pm? Also, the weather is nice." Output: ["Buy
milk (Deadline: 5pm)"]

Input: "Just checking in. Please submit the report tomorrow." Output: ["Submit
report (Deadline: Tomorrow)"]

# INPUT

[User's Email]