Prompts · Local LLM (small models)
Code explainer for small coder models
Model notes: Qwen2.5-Coder 7B, Devstral, Qwen3-Coder
Fill in the variables (optional — they're inserted live below):
Explain what this {{language}} function does.
```{{language}}
{{code}}
```
Answer in this format:
Purpose: (one sentence)
Inputs: (list)
Output: (one sentence)
Steps: (numbered, max 6)
Possible bugs: (list, or "none spotted")
How to use it
- Fill the variables above, or replace each
{{variable}}after pasting. - Paste into your AI tool (ChatGPT, Claude, Gemini, Cursor, Claude Code, or a local model in Ollama/LM Studio).
- Review the output. Models make mistakes, especially with facts, numbers and code.
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More Local LLM (small models) prompts
Local LLM (small models)
Small-model friendly summariser
Tuned for 3B-9B local models (Gemma 4 E4B, Qwen3.5 9B, Llama 3.2 3B)
Variables: text
Summarise the text below.
Rules:
- Exactly 5 bullet points.
- Each bullet is one sentence, under 20 words.
- Only use facts from the text.
Text:
"""
{{text}}
"""
Summary: Summarise the text below.
Rules:
- Exactly 5 bullet points.
- Each bullet is one sentence, under 20 words.
- Only use facts from the text.
Text:
"""
{{text}}
"""
Summary:
Open & fill variables → Local LLM (small models)
Structured JSON extraction (small models)
Use Ollama's format/JSON-schema option for reliability
Variables: text
Extract the fields below from the text. Reply with JSON only, no explanation.
Fields:
- name (string)
- email (string or null)
- company (string or null)
- request (string, max 15 words)
Example output:
{"name": "Sam Lee", "email": "[email protected]", "company": null, "request": "wants a quote for 20 licences"}
Text:
"""
{{text}}
""" Extract the fields below from the text. Reply with JSON only, no explanation.
Fields:
- name (string)
- email (string or null)
- company (string or null)
- request (string, max 15 words)
Example output:
{"name": "Sam Lee", "email": "[email protected]", "company": null, "request": "wants a quote for 20 licences"}
Text:
"""
{{text}}
"""
Open & fill variables → Local LLM (small models)
Classify into fixed labels
Reliable on 1B-4B models with temperature 0
Variables: labelsmessage
Classify the message into exactly one label.
Labels: {{labels}}
Reply with the label only.
Message: {{message}}
Label: Classify the message into exactly one label.
Labels: {{labels}}
Reply with the label only.
Message: {{message}}
Label:
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