Prompts · Local LLM (small models)
Classify into fixed labels
Model notes: Reliable on 1B-4B models with temperature 0
Fill in the variables (optional — they're inserted live below):
Classify the message into exactly one label.
Labels: {{labels}}
Reply with the label only.
Message: {{message}}
Label:
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": "sam@example.com", "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": "sam@example.com", "company": null, "request": "wants a quote for 20 licences"}
Text:
"""
{{text}}
"""
Open & fill variables → Local LLM (small models)
Local RAG answer prompt
For chat models in a local RAG pipeline
Variables: retrieved_chunksquestion
Use the context to answer the question. If the answer is not in the context, say "I don't know based on these documents."
Quote the sentence you used in "quotes".
Context:
{{retrieved_chunks}}
Question: {{question}}
Answer: Use the context to answer the question. If the answer is not in the context, say "I don't know based on these documents."
Quote the sentence you used in "quotes".
Context:
{{retrieved_chunks}}
Question: {{question}}
Answer:
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