korchasa 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]