This AI-powered workflow automates customer support by combining internal knowledge base search, Google Docs knowledge retrieval, API integration, and advanced language model reasoning. The agent responds in Slovak or the customer’s language, always provides up-to-date information, and can escalate to human support if needed. Ideal for companies seeking multilingual, automated, and context-aware customer service.
Flows
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.
Create a prompt template with dynamic variables ({input}, {human_input}, {context}, {chat_history}, {system_message}, {all_input_variables}).
---CONVERSATION HISTORY---
{context}
---
---LATEST MESSAGE---
{input}
---
Create a prompt template with dynamic variables ({input}, {human_input}, {context}, {chat_history}, {system_message}, {all_input_variables}).
---CHAT HISTORY---
{chat_history}
---
---CUSTOMER QUERY---
{context}
---
---LLM RESPONSE---
{input}
---
The final output presented to the agent should include:
the original CUSTOMER QUERY
the Slovak translation of the CUSTOMER QUERY
LLM RESPONSE in Slovak
LLM RESPONSE in the customer's language (to be sent)
Create a prompt template with dynamic variables ({input}, {human_input}, {context}, {chat_history}, {system_message}, {all_input_variables}).
https://arshiakahani.ladesk.com/api/v3/tickets/{input}/messages
Create a prompt template with dynamic variables ({input}, {human_input}, {context}, {chat_history}, {system_message}, {all_input_variables}).
https://YOURCOMPANY.ladesk.com/api/conversations/{human_input}/messages
Generate text using input prompt and selected LLM model.
extract the "message" section of the "type": "M" only the section exactly as it is and only and only output the latest message based on the date with nothing else but the users message itself.
A tool calling agent.
You are an AI language model assistant acting as a friendly and professional customer support and shopping assistant for YOURCOMPANY. You respond in Slovak language by default, or in the customer's input language if detected to be different than Slovak. AND ALWAYS USE EMAIL TONE AND FORMAT.
<u>Your role:</u>
You combine the responsibilities of technical customer support and product recommendation assistant. You help customers solve issues, make decisions, and complete purchases related to YOURCOMPANY products and services. Your tone is always friendly and professional, and your goal is to ensure the customer feels understood, supported, and confident in their next step.
<u>Your Goal:</u>
you receive CONVERSATION HISTORY and the most recent user query as LATEST MESSAGE your goal is to answer the LATEST MESSAGE based on the tools at your disposal. 
<u>Identify intent and provide answers:</u>
First source: ALWAYS SEARCH THE knowledge_source_tool TO ANSWER USER'S QUESTION AND NEVER ANSWER FROM YOURSELF.
Second source: Always use the Document Retriever tool to find context related to the question.
If relevant context is found:
Use it to provide accurate, concise answers.
Include ONLY RELEVANT URLs retrieved from the Document Retriever, never edit the url.
Never invent product names and category names. You can recognize a category by the fact that the page MUST contain a list of different products.; use only those available in your knowledge base.
Follow the information exactly as stated in the reference.
If no relevant context is found and the question is about YOURCOMPANY:
Ask polite clarifying questions to gather more details.
If still unresolved, use the Contact Human Assist tool to transfer to a human support agent.
If the customer’s message is unclear or incomplete:
Do not guess — always ask for more information before answering.
If the customer shows interest in a specific product:
Let them know that pricing and ordering is quick and simple directly on the website.
They can configure the product (dimensions, extras, quantity…) and see the price immediately and the production time.
If the question is about production time, always include express options if available.
For inquiries not related to YOURCOMPANY:
Politely inform the customer that you only provide support for YOURCOMPANY.
Suggest contacting the appropriate business support team at CONTACT METHOD
<u>Resource Utilization:</u>
Use the Document Retriever to search for knowledge relevant to the customer question.
Use the Contact Human Assist tool to escalate if needed.
Use the Document Retriever to provide valid product or info links - NEVER invent or assume URLs
<u>Formatting:</u>
Your tone is always friendly, clear, and professional.
The answers should be SHORT - max. about 100-200 tokens.
Use structured formatting:
Short paragraphs
Bold text for emphasis
Bullet points where appropriate
Emojis to make the messages more engaging 😊
Write in plain text format. Do not use markdown.
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.
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.
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.
The Create Data component enables you to dynamically generate structured data records with a customizable number of fields. Ideal for workflows that require the creation of new data objects on the fly, it supports flexible field configuration and seamless integration with other automation steps.
Integrate external data and services into your workflow with the API Request component. Effortlessly send HTTP requests, set custom headers, body, and query parameters, and handle multiple methods like GET and POST. Essential for connecting your automations to any web API or service.
The Parse Data component transforms structured data into plain text using customizable templates. It enables flexible formatting and conversion of data inputs for further use in your workflow, helping to standardize or prepare information for downstream components.
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.
FlowHunt supports dozens of text generation models, including models by OpenAI. Here's how to use ChatGPT in your AI tools and chatbots.
The Chat History component in FlowHunt enables chatbots to remember previous messages, ensuring coherent conversations and improved customer experience while optimizing memory and token usage.
Explore the Tool Calling Agent in FlowHunt—an advanced workflow component that enables AI agents to intelligently select and use external tools to answer complex queries. Perfect for building smart AI solutions that require dynamic tool usage, iterative reasoning, and integration with multiple resources.
FlowHunt's Document Retriever enhances AI accuracy by connecting generative models to your own up-to-date documents and URLs, ensuring reliable and relevant answers using Retrieval-Augmented Generation (RAG).
Integrate your workflows with Google Docs using the Google Docs Retriever component—seamlessly fetch document content for use in automations, chatbots, or knowledge workflows. Ideal for accessing, processing, and leveraging your Google Docs within FlowHunt flows.
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.
Flow description
This workflow automates the process of retrieving customer messages from a ticketing or support system, extracting the latest relevant message, enhancing it with context and chat history, and then leveraging advanced AI (LLMs) combined with knowledge tools to generate professional, multilingual customer support responses. The process then prepares and sends these responses back to external systems, making it ideal for scaling and automating customer support, knowledge retrieval, and external API integration.
https://arshiakahani.ladesk.com/api/v3/tickets/{input}/messages). This enables flexible retrieval based on incoming user data.| Component | Purpose |
|---|---|
| Chat Input | Accepts user/customer messages |
| Prompt Template | Dynamically builds URLs and message prompts |
| API Request | Fetches ticket data/messages from external system |
| Parse Data | Converts structured data into plain text |
| OpenAI LLM | Extracts relevant messages, generates or translates responses |
| Document Retriever | Searches knowledge base for relevant info |
| Google Docs Retriever | Integrates external docs as knowledge for the agent |
| Tool Calling Agent | Central AI support agent—uses tools and chat history |
| Create Data | Packages responses and data for outbound API requests |
| Chat Output | Displays the final result to end-user or system |
| Notes | Provide operator guidance (e.g., where to input API keys/URLs) |
Below is a simplified flowchart representation of the main steps:
This workflow is a robust foundation for any organization looking to automate and scale customer support, technical assistance, or information delivery workflows that require integration with external APIs, knowledge bases, and advanced AI responses.
We help companies like yours to develop smart chatbots, MCP Servers, AI tools or other types of AI automation to replace human in repetitive tasks in your organization.
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