This workflow allows users to manually trigger a query to an AI language model with a predefined message. It sends the message, retrieves the AI's response, and then summarizes the content of that response. It's useful for real-time AI prompt testing or educational demonstrations.
This workflow accepts a URL to a research paper, extracts its content using an OCR reader, and then leverages AI to create various summaries. It generates different summary types, such as executive, technical, layperson, and social media posts, to make complex academic content accessible to diverse audiences.
This workflow is manually triggered by providing a brand URL. It then uses GenFuse's scraping capabilities to extract content from that URL. The extracted content is sent to an AI model (like Google Gemini) for summarization and sentiment analysis, providing insights into brand perception. The final analyzed content can then be displayed or sent to another app.
This workflow allows you to manually input a phrase for language learning. An AI then generates a detailed description and example sentences for the phrase. Finally, the phrase and its AI-generated content are saved to a Google Sheet, suitable for export to Anki.
This workflow enables users to input a research query and automatically receive a comprehensive, AI-generated report. It utilizes an AI search engine to gather information and an LLM to synthesize and format the data into a well-structured document, ready for review.
This workflow transforms a simple user query into comprehensive, well-structured HTML content. It leverages Perplexity AI for in-depth research, then uses an LLM (such as GPT) to organize the findings and generate a professional blog post in HTML format, complete with proper semantic structure and formatting.
This workflow enables users to perform an AI-powered search using Perplexity AI. Triggered manually, it takes a user's question, sends it to Perplexity AI, and then provides a direct, real-time answer as the workflow's outcome.