AI Product Use Case Generator

Generate comprehensive, AI-driven reports on software product use cases for marketing and sales. This workflow researches the product across web sources and YouTube, analyzes current trends, and produces a detailed article highlighting practical applications and key features. Ideal for marketers and sales teams seeking actionable insights and competitive positioning.

How the AI Flow works - AI Product Use Case Generator

Flows

How the AI Flow works

Collect Product Input.
Captures the product name from the user to initiate research.
Research Online and YouTube Use Cases.
Uses AI agents to search Google, retrieve web content, and explore YouTube for relevant use cases in marketing and sales.
Analyze and Aggregate Insights.
AI agents process and summarize findings, identifying the most relevant use cases and trends from the gathered sources.
Collaborative Task Management.
Organizes AI agents' tasks and coordinates their findings for a thorough and structured report.
Generate and Deliver Comprehensive Report.
Uses advanced AI language models to produce a detailed, SEO-optimized article covering the product's features and applications, and presents the result to the user.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Prompt

Prompt template to extract practical use cases of a product in marketing, sales, and YouTube, with product context and features.

                Given the topic in the input, Extract the following data based on the product name:

1. Practical use cases of {input} in Marketing
2. Practical use cases of {input} in Sales
3. Practical use cases of {input} from Youtube
Otter.ai is tailored for business professionals, journalists, students, and anyone heavily engaged in meetings, interviews, or lectures. Its ability to automate note-taking allows users to focus on the conversation without the distraction of manual transcription. By capturing and organizing information efficiently, Otter.ai enhances collaboration and ensures that critical insights are not lost, making it an invaluable tool for individuals and teams striving for productivity.
Main Features of Otter.ai

    Live Transcription: Converts speech to text in real-time during meetings and media playback.
    Otter Assistant: Automatically joins meetings on your behalf to take notes, even if you can’t attend.
    Custom Vocabulary: Learns and recognizes specific terminology and jargon unique to your field.
    Summaries and Action Items: Generates concise summaries with key points and actionable tasks.
    Integrations: Works seamlessly with platforms like Zoom, Google Meet, and Microsoft Teams.
    Accessibility: Available on both mobile devices and web browsers, ensuring access anytime, anywhere.
    Collaborative Editing: Allows multiple users to edit and share notes, enhancing team collaboration.
    Speaker Identification: Differentiates between speakers for clearer understanding of dialogue.

TOPIC: {input}

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

Message Widget

The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.

See Message Widget

Chat Output

Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.

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Chat Opened Trigger

The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.

See Chat Opened Trigger

URL Retriever

Unlock web content in your workflows with the URL Retriever component. Effortlessly extract and process the text and metadata from any list of URLs—including web articles, documents, and more. Supports advanced options like OCR for images, selective metadata extraction, and customizable caching, making it ideal for building knowledge-rich AI flows and automations.

See URL Retriever

AI Agent

The AI Agent component in FlowHunt empowers your workflows with autonomous decision-making and tool-using capabilities. It leverages large language models and connects to various tools to solve tasks, follow goals, and provide intelligent responses. Ideal for building advanced automations and interactive AI solutions.

See AI Agent

GoogleSearch Component

FlowHunt's GoogleSearch component enhances chatbot accuracy using Retrieval-Augmented Generation (RAG) to access up-to-date knowledge from Google. Control results with options like language, country, and query prefixes for precise and relevant outputs.

See GoogleSearch Component

LLM OpenAI

FlowHunt supports dozens of text generation models, including models by OpenAI. Here's how to use ChatGPT in your AI tools and chatbots.

See LLM OpenAI

Self-Managed Crew

Unlock advanced collaboration in FlowHunt with the Self-Managed Crew component. Coordinate multiple AI agents under a manager agent to autonomously handle complex workflows and hierarchical tasks, maximizing efficiency and scalability.

See Self-Managed Crew

YouTube Search Tool

FlowHunt's AI-driven YouTube Search Tool offers personalized video and creator recommendations. Go beyond standard YouTube searches and let AI agents find the best creators and content for your interests.

See YouTube Search Tool

Generator

Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.

See Generator

SelfManaged Task

The SelfManaged Task component enables users to define and execute autonomous tasks within a workflow. Specify a clear task description, expected outcome, and assign an agent to manage execution—ideal for building structured, hierarchical automation in your flows.

See SelfManaged Task

Prompt Component in FlowHunt

Learn how FlowHunt's Prompt component lets you define your AI bot’s role and behavior, ensuring relevant, personalized responses. Customize prompts and templates for effective, context-aware chatbot flows.

See Prompt Component in FlowHunt

ChatInput

The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.

See ChatInput

Flow description

Purpose and benefits

Overview

The Use Case Generator workflow is designed to automatically generate a comprehensive, in-depth report on a given product by aggregating and analyzing data from multiple online sources. It highlights the product’s features and practical applications, specifically focusing on marketing and sales use cases, and incorporates insights from YouTube content. The process is fully automated and leverages AI agents, web search, content extraction, and advanced language models to deliver structured and actionable market insights.

Key Workflow Steps

  1. User Interaction & Input

    • The process starts with a welcoming message, instructing the user to enter the product name.
    • The user provides the product name via a chat input interface.

  2. Prompt Preparation

    • Two specialized prompts are created:
      • The first prompt requests extraction of practical use cases for the product in marketing, sales, and YouTube contexts, as well as a summary of its main features and target users.
      • The second prompt is for generating a copywriter-style article with well-structured headings about the product, drawing from the aggregated insights.

  3. Data Gathering

    • Web Search: The workflow uses Google Search to find relevant URLs about the product.
    • Content Retrieval: The content from 10-30 URLs is fetched and processed, providing a broad and up-to-date data set.
    • YouTube Search: The workflow searches for relevant YouTube videos and extracts information from titles and descriptions.

  4. AI-Driven Analysis

    • Three AI agents are activated:
      • Marketing Use Case Agent: Analyzes web content for latest marketing use cases of the product.
      • Sales Use Case Agent: Focuses on sales-related use cases.
      • YouTube Use Case Agent: Digs into YouTube video metadata to identify additional practical use cases.
    • These agents are orchestrated as a collaborative “crew,” each contributing their specialized findings to the final report.

  5. Task Management

    • A self-managed task system ensures that the expected output—a thorough product report—is generated efficiently, with clear task descriptions and agent roles.

  6. Content Synthesis

    • The agents’ collective findings are compiled.
    • A language model (OpenAI’s o1-preview) receives the context and prompt to generate a well-structured article, featuring:
      • An H2 heading (“What is [Product Name]?”)
      • H3 subheadings tailored to the product
      • Integrated insights from all data sources

  7. Output Delivery

    • The generated report is displayed to the user in the chat interface, ready for use in marketing, sales, or product documentation.

Workflow Diagram

StepTools/Nodes UsedPurpose
User inputChatOpenedTrigger, MessageWidget, ChatInputCollects product name and initiates the flow
Prompt creationPromptTemplateCreates structured prompts for analysis
Web & YouTube searchGoogleSearch, YouTubeSearchToolFinds URLs and videos about the product
Content retrievalURLContentExtracts content from URLs
Agent analysisAIAgent (x3)Specialized agents for marketing, sales, YT
Crew coordinationSelfManagedCrew, SelfManagedTaskOrganizes agents/tasks for collaboration
Content generationOpenAILLM, GeneratorProduces a publish-ready article
Output presentationChatOutputDisplays the final report

Why This Workflow is Useful

  • Scalability: By automating the process of data collection, analysis, and report generation, this workflow can handle multiple product evaluations quickly and with minimal human intervention.
  • Comprehensiveness: The workflow taps into diverse data streams—web, documents, and YouTube—ensuring a broad and current perspective.
  • Consistency: Structured prompts and AI agents ensure that every report follows a professional, repeatable format.
  • Collaboration: The crew-based agent structure allows parallel processing of different research domains (marketing, sales, multimedia).
  • Actionable Output: The output is ready to use for marketing materials, sales enablement, or product documentation, saving hours of manual research and writing.

Example Use Cases

  • Marketing Teams: Quickly generate product briefs or competitive analyses for campaigns.
  • Sales Enablement: Create up-to-date product one-pagers for training or client presentations.
  • Product Managers: Get a 360-degree view of how a product is perceived and used in the market.
  • Content Creators: Use the generated article as a base for blog posts or explainer content.

Conclusion

This workflow streamlines the process of researching, analyzing, and communicating a product’s use cases and features. By leveraging automation and AI, it empowers teams to produce high-quality, data-driven reports at scale, freeing up valuable time for strategic tasks.

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