Prompt
Create a prompt template with dynamic variables for LLM input.
what you see in the {input} picture, describe it, if it is a CAPTCHA return the code only
AI Captcha Image Solver
This AI-powered workflow automatically solves CAPTCHA images uploaded by users. It guides users with instructions, processes the uploaded image using a prompt and large language model, and returns the interpreted text or code from the CAPTCHA, streamlining access and verification processes.
Flows
How the AI Flow works
- Initiate Chat.
- Detects when a user opens the chat and prepares the interface for interaction.
- Display Instructions.
- Shows a welcome message with instructions for uploading a CAPTCHA image.
- Receive Image Input.
- Collects the uploaded CAPTCHA image from the user.
- Analyze CAPTCHA.
- Processes the uploaded image using a prompt and an AI text generator to interpret the CAPTCHA content.
- Return Solution.
- Displays the decoded CAPTCHA text or code back 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.
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.
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.
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.
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.
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.
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.
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.
Flow description
Purpose and benefits
Overview
This workflow, titled “Captcha Solver”, is designed to automate the process of solving CAPTCHA images sent by a user. The flow enables a conversational interface where users can upload a CAPTCHA image and receive the interpreted text or code, making it particularly useful for tasks that require scalable and automated CAPTCHA recognition.
Step-by-Step Flow
1. User Onboarding and Welcome Message
- Trigger: When a chat session is opened, the workflow starts with the Chat Opened Trigger node.
- Welcome Message: This trigger passes the session to a Message Widget, which displays a friendly greeting and instructions. The message tells the user:
“This is a CAPTCHA solver 🤩. In order to assist you, please send me an image 📷 that contains the CAPTCHA you need help with. I will analyze the image and try to solve the CAPTCHA for you. Once you send the image, I’ll do my best to interpret the text or numbers displayed, and provide you with the solution 🧠🔍.” - Display: The message is then sent to the chat output so the user sees it immediately upon joining.
2. User Input Handling
- Receiving Inputs: The Chat Input node collects user inputs, which may include text and file uploads (such as images).
- File Upload: If the user sends an image file, it is routed to two places:
- The Prompt Template component, as a reference for prompt construction.
- Directly to the Generator (AI model), which processes images.
3. Prompt Preparation
- Prompt Design: The Prompt Template node uses the uploaded image as
{input}in a dynamic prompt:“what you see in the {input} picture, describe it, if it is a CAPTCHA return the code only”
- Contextual Input: The prompt is tailored to instruct the model to only return the CAPTCHA code if present.
4. AI Processing
- Generator Node: The prepared prompt and the uploaded image are passed to the Generator node, which uses a Large Language Model (LLM) with image input capability.
- Interpretation: The AI processes the image and returns a text output, ideally the deciphered CAPTCHA code.
5. Output Delivery
- Response: The result from the generator (the solved CAPTCHA code) is sent to the Chat Output node, where the user sees the answer.
- File Echo: The original image is also routed to the output, ensuring users can confirm which image was processed.
Flow Diagram (Summary Table)
| Step | Node/Component | Purpose |
|---|---|---|
| Chat Opened | Chat Opened Trigger | Starts the flow when session opens |
| Welcome Message | Message Widget | Informs and guides the user |
| Show Instructions | Chat Output | Displays welcome/instructions to the user |
| Receive User Input | Chat Input | Accepts text and image (CAPTCHA) input |
| Prepare Prompt | Prompt Template | Builds AI prompt with dynamic image reference |
| AI Processing | Generator | Uses LLM to interpret and solve the CAPTCHA |
| Show Results | Chat Output | Displays the solved CAPTCHA code to the user |
| Echo Uploaded Image | Chat Output | Optionally shows the original uploaded image |
Benefits and Use Cases
- Scalability: The flow automates CAPTCHA solving, reducing manual effort and enabling bulk or repeated processing.
- User-Friendly: With clear onboarding and feedback, users are guided step-by-step without confusion.
- Integration: The use of LLMs for image-to-text conversion means the flow can adapt to many different CAPTCHA types without custom coding.
- Automation: Useful for QA, testing, accessibility, or any context where repetitive CAPTCHA recognition would otherwise be a bottleneck.
Conclusion
This workflow efficiently automates the process of interpreting CAPTCHA images through a conversational interface, leveraging AI for image understanding. It is a scalable solution for anyone needing to process large numbers of CAPTCHAs, integrate CAPTCHA-solving into other automations, or simply reduce the friction of manual entry.
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