mem0/docs/components/rerankers/custom-prompts.mdx

---
title: Custom Prompts
description: "Customize the LLM reranker prompt template in Mem0 to control how search results are ranked and scored."
---

When using LLM rerankers, you can customize the prompts used for ranking to better suit your specific use case and domain.

## Default Prompt

The default LLM reranker prompt scores each memory individually on a 0.0-1.0 scale:

```
You are a relevance scoring assistant. Given a query and a document, you need to score how relevant the document is to the query.

Score the relevance on a scale from 0.0 to 1.0, where:
- 1.0 = Perfectly relevant and directly answers the query
- 0.8-0.9 = Highly relevant with good information
- 0.6-0.7 = Moderately relevant with some useful information
- 0.4-0.5 = Slightly relevant with limited useful information
- 0.0-0.3 = Not relevant or no useful information

Query: "{query}"
Document: "{document}"

Provide only a single numerical score between 0.0 and 1.0. Do not include any explanation or additional text.
```

## Custom Prompt Configuration

<Warning>
  The `scoring_prompt` field is **deprecated** and will be removed in a future version. Setting it still works but emits a `DeprecationWarning` at runtime. The field currently sets the system prompt used by the LLM; custom prompts may be exposed through a different API in a future release.
</Warning>

You can provide a custom prompt template using the `scoring_prompt` parameter:

```python
from mem0 import Memory

custom_prompt = """
You are an expert at evaluating memories for a personal AI assistant.
Given a user query and a memory entry, score how relevant the memory is.
Consider direct relevance, temporal relevance, and actionability.

Query: "{query}"
Memory: "{document}"

Provide only a single numerical score between 0.0 and 1.0.
"""

config = {
    "reranker": {
        "provider": "llm_reranker",
        "config": {
            "provider": "openai",
            "model": "gpt-4o-mini",
            "api_key": "your-openai-key",
            "scoring_prompt": custom_prompt,
            "top_k": 5
        }
    }
}

memory = Memory.from_config(config)
```

## Prompt Variables

Your custom prompt can use the following variables:

| Variable     | Description                   |
| ------------ | ----------------------------- |
| `{query}`    | The search query              |
| `{document}` | The memory entry being scored |

<Note>
  Both `{query}` and `{document}` are required in your custom prompt. The LLM reranker scores each memory individually against the query, so the prompt is called once per candidate memory.
</Note>

## Domain-Specific Examples

### Customer Support

```python
customer_support_prompt = """
You are ranking customer support conversation memories.
Prioritize memories that:
- Relate to the current customer issue
- Show previous resolution patterns
- Indicate customer preferences or constraints

Query: "{query}"
Memory: "{document}"

Score relevance from 0.0 to 1.0.
"""
```

### Educational Content

```python
educational_prompt = """
Score this learning memory for relevance to a student query.
Consider:
- Prerequisite knowledge requirements
- Learning progression and difficulty
- Relevance to current learning objectives

Student Query: "{query}"
Memory: "{document}"

Score educational relevance from 0.0 to 1.0.
"""
```

### Personal Assistant

```python
personal_assistant_prompt = """
Score this personal memory for relevance to the user's query.
Consider:
- Recent vs. historical importance
- Personal preferences and habits
- Contextual relationships

Query: "{query}"
Memory: "{document}"

Provide relevance score from 0.0 to 1.0.
"""
```

## Advanced Prompt Techniques

### Multi-Criteria Scoring

```python
multi_criteria_prompt = """
Evaluate this memory using multiple criteria:

1. RELEVANCE (40%): How directly related to the query
2. RECENCY (20%): How recent the memory appears to be
3. IMPORTANCE (25%): Personal or business significance
4. ACTIONABILITY (15%): How useful for next steps

Query: "{query}"
Memory: "{document}"

Compute a weighted score from 0.0 to 1.0 based on these criteria.
Provide only the final numerical score.
"""
```

### Chain-of-Thought Scoring

```python
reasoning_prompt = """
Evaluate this memory's relevance step by step:

1. What is the main intent of the query?
2. What key information does the memory contain?
3. How directly does the memory address the query?

Based on this analysis, provide a single relevance score from 0.0 to 1.0.

Query: "{query}"
Memory: "{document}"

Score:
"""
```

## Best Practices

1. **Be Specific**: Clearly define what makes a memory relevant for your use case
2. **Use 0.0-1.0 Scale**: The score extractor expects values between 0.0 and 1.0
3. **Request Only the Score**: Ask for just the numerical score to improve extraction reliability
4. **Test Iteratively**: Refine your prompt based on actual ranking performance
5. **Consider Token Limits**: Keep prompts concise while being comprehensive

## Prompt Testing

You can test different prompts by comparing ranking results:

```python
# Test multiple prompt variations
prompts = [
    default_prompt,
    custom_prompt_v1,
    custom_prompt_v2
]

for i, prompt in enumerate(prompts):
    config["reranker"]["config"]["scoring_prompt"] = prompt
    memory = Memory.from_config(config)

    results = memory.search("test query", filters={"user_id": "test_user"}, rerank=True)
    print(f"Prompt {i+1} results: {results}")
```

## Common Issues

- **Too Long**: Keep prompts under token limits for your chosen LLM
- **Too Vague**: Be specific about scoring criteria
- **Wrong Scale**: Use 0.0-1.0 scale to match the default score extractor
- **Extra Output**: Ask for only the numeric score — extra text can confuse score extraction