Prompts

Caesar is a horizontal general solution for research that works across domains without requiring specialized prompt engineering. While Caesar doesn’t need complex prompting strategies, being explicit in your queries will yield better, more targeted results.

Writing effective queries

Caesar performs best when queries are clear and unambiguous. While the system is designed to understand context, explicit queries reduce ambiguity and improve result quality.

Be specific about your domain

When your query involves terms that could have multiple interpretations, provide context to guide the research.

What are the top tokens in the virtuals agent ecosystem?

The first query clarifies you’re asking about tokens within the “virtuals agent ecosystem,” avoiding ambiguity around what “virtuals” means.

More examples of explicit queries

Output transformation with system_prompt

The POST /research endpoint accepts an optional system_prompt parameter that allows you to transform the final output format.

How it works

When you provide a system_prompt:

1. Caesar performs the research using your query
2. The research results are stored in the content field
3. The system prompt is applied to transform the content
4. The transformed result appears in transformed_content

Request example

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from caesar import Caesar
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client = Caesar(
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    api_key="My API Key",
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)
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research = client.research.create(
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    query="Is lithium supply a bottleneck for EV adoption?",
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)
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print(research.id)

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{
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  "query": "Latest developments in quantum computing for drug discovery",
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  "system_prompt": "Format the response as a bullet-point executive summary with key findings and implications"
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}

Response structure

When using system_prompt, the response includes both original and transformed content:

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{
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  "id": "abc123...",
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  "status": "completed",
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  "query": "Latest developments in quantum computing for drug discovery",
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  "content": "Full research content with detailed analysis...",
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  "transformed_content": "" Key Finding 1: IBM's quantum advantage in molecular simulation...\n" Key Finding 2: ...",
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  "results": [...]
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}

Common transformation patterns

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{
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  "system_prompt": "Provide a 3-paragraph executive summary highlighting key insights, implications, and recommendations"
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}

Key principles

Tips for optimal results

1. Include relevant context: Mention time periods, geographic regions, or specific sectors when relevant
2. Use industry-standard terminology: Caesar understands domain-specific terms and acronyms
3. Specify comparison criteria: When comparing options, state what metrics or factors matter
4. Set clear boundaries: If you want specific scope (e.g., “only peer-reviewed sources”), state it explicitly