Overview
The LLM Search block sends a single prompt to several answer-engine platforms in parallel and returns each platform’s answer plus its citations. It also returns one combined, deduplicated list of citation URLs. Use it to see how AI engines answer a query and to gather source URLs for further research.Configuration
Prompt (Required)
The question or instruction to send to each platform. Example:{{step_1.output}}
Platforms
Select which answer engines to query. They run in parallel. Select at least one. Default: All platformsCountry
A 2-letter country code for localized results. Default: United States (us)
Options: United States, United Kingdom, Canada, Australia, India, Germany, France
Error Handling
Define what happens if the block fails. Options:- Terminate Workflow: Stop execution immediately.
- Continue Execution: Proceed to the next step despite the error.
error field while other platforms still return results.
Output Data
Returns a JSON object.prompt
The rendered prompt that was sent.platforms
The list of platforms that were queried.results
An object keyed by platform. Each entry has ananswer and a citations array of { url, title }. A failed platform also includes an error field.
citation_urls
A combined, deduplicated list of citation URLs from all platforms, ready for scraping. Accessing fields:{{step_1.output.results.chatgpt.answer}}, {{step_1.output.citation_urls[0]}}
Sample Output
Best Practices
- Write a specific prompt; vague prompts return generic answers across engines.
- Select only the platforms you need to keep runs fast and focused.
- Use
citation_urlsto feed a Loop and Web Scrape for deeper research. - Set the Country to match the market you are studying.
- Compare answers across platforms to spot consensus and gaps.
Common Use Cases
What’s Next
- Scrape citation URLs with the Web Scrape Block
- Loop through
citation_urlswith the Loop Block - Summarize answers with the LLM Block
- Save results to Google Sheets
- Learn about Variable Referencing