Unlike traditional LLMs, our web search models require specificity to retrieve relevant search results. Adding just 2-3 extra words of context can dramatically improve performance.

Good Example: “Explain recent advances in climate prediction models for urban planning”

Poor Example: “Tell me about climate models”

While few-shot prompting works well for traditional LLMs, it confuses web search models by triggering searches for your examples rather than your actual query.

Good Example: “Summarize the current research on mRNA vaccine technology”

Poor Example: “Here’s an example of a good summary about vaccines: [example text]. Now summarize the current research on mRNA vaccines.”

Craft prompts with search-friendly terms that would appear on relevant web pages. Consider how experts in the field would describe the topic online.

Good Example: “Compare the energy efficiency ratings of heat pumps vs. traditional HVAC systems for residential use”

Poor Example: “Tell me which home heating is better”

Include critical context to guide the web search toward the most relevant content, but keep prompts concise and focused.

Good Example: “Explain the impact of the 2023 EU digital markets regulations on app store competition for small developers”

Poor Example: “What are the rules for app stores?”

Generic prompts lead to scattered web search results and unfocused responses. Always narrow your scope.

Avoid: “What’s happening in AI?”

Instead: “What are the three most significant commercial applications of generative AI in healthcare in the past year?”

Prompting strategies designed for traditional LLMs often don’t work well with web search models. Adapt your approach accordingly.

Avoid: “Act as an expert chef and give me a recipe for sourdough bread. Start by explaining the history of sourdough, then list ingredients, then…”

Instead: “What’s a reliable sourdough bread recipe for beginners? Include ingredients and step-by-step instructions.”

Complex prompts with multiple unrelated questions can confuse the search component. Focus on one topic per query.

Avoid: “Explain quantum computing, and also tell me about regenerative agriculture, and provide stock market predictions.”

Instead: “Explain quantum computing principles that might impact cryptography in the next decade.”

Don’t assume the model will search for what you intended without specific direction. Be explicit about exactly what information you need.

Avoid: “Tell me about the latest developments.”

Instead: “What are the latest developments in offshore wind energy technology announced in the past 6 months?”

Adjust model parameters based on your specific needs:

• Search Domain Filter: Limit results to trusted sources for research-heavy queries.
• Search Context Size: Use “high” for comprehensive research questions and “low” for simple factual queries.

Example configuration for technical documentation:

{
  "search_domain_filter": ["wikipedia.org", "docs.python.org"],
  "web_search_options": {
    "search_context_size": "medium"
  }
}