AI Customer Service Chatbot

An AI-powered customer service chatbot that uses your internal knowledge sources to provide instant, accurate, and helpful responses to customer queries. It leverages chat history, retrieves relevant documents, and uses AI to generate responses, delivering efficient support and improved customer satisfaction.

Flows

How the AI Flow works

Welcome Message Triggered.: When a chat is opened, the bot greets the user with a welcome message.
Collect User Query.: The chatbot collects the user's input or question in the chat interface.
Retrieve Relevant Knowledge.: The bot searches internal knowledge sources for information relevant to the user's question.
Generate AI Response.: AI generates a concise and accurate response using the user question, retrieved knowledge, and chat history.
Display Answer.: The chatbot displays the AI-generated answer to the user in the chat.

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

Create a prompt template with dynamic variables ({input}, {human_input}, {context}, {chat_history}, {system_message}).

                You are a helpful customer AI customer service bot representing [company]. Your name is [bot name]. As a customer service agent, provide the client with concise and relevant answers based on knowledge. If you don't know the answer, provide a link to support.

---CHAT HISTORY---
{chat_history}
---

---KNOWLEDGE---
{context}
----


USER: {input}

ANSWER:

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.

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

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.

See Chat Output

Chat History Component

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.

See Chat History Component

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

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

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

Document Retriever

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).

See Document Retriever

Flow description

Purpose and benefits

This workflow defines a Customer Service Chatbot that leverages internal knowledge sources to provide users with accurate, helpful, and context-aware responses. The flow is designed to automate customer interactions, streamline support, and ensure that users receive timely and relevant answers.

Main Flow Steps

1. Chat Session Initiation and Welcome

Chat Opened Trigger: The flow begins by detecting when a user opens a chat session.
Welcome Message: A friendly, informative welcome message is displayed to the user, inviting them to ask questions and explaining the chatbot’s purpose.
Output Display: The welcome message is shown in the chat interface, ensuring users feel guided from their very first interaction.

2. Handling User Input

Chat Input: Whenever a user sends a message or query, it is captured for processing.
Chat History: The conversation history is retrieved and made available for context, enabling the bot to provide coherent, continuous support.
Document Retriever: The user’s query is used to search connected knowledge sources or documentation, retrieving the most relevant information for the current question.

3. Dynamic Prompt Construction

Prompt Template: A dynamic prompt is assembled using several variables:
- {input}: The user’s latest message.
- {chat_history}: The ongoing conversation with the user.
- {context}: Knowledge snippets retrieved from internal sources.
- The prompt instructs the AI to act as a helpful customer service bot, answer concisely, and offer a support link if the answer is not known.

Prompt Example Structure

Section	Content
System Message	“You are a helpful AI customer service bot…”
Chat History	Recent conversation with the user
Knowledge	Retrieved documents or knowledge snippets
User Input	User’s current question
Answer	Response generated by the AI

4. AI Response Generation

Generator Node: The constructed prompt is sent to an AI model (LLM), which generates a relevant and accurate reply using the provided context and history.

5. Presenting the Response

Chat Output: The generated answer is displayed back to the user in the chat interface, closing the response loop and awaiting further user input if necessary.

Workflow Visualization

Below is a simplified sequence of the main nodes and their roles:

Step	Node/Component	Purpose
1	Chat Opened Trigger	Detects new chat sessions and starts workflow
2	Message Widget	Sends welcome message
3	Chat Output	Displays messages to the user
4	Chat Input	Receives user messages
5	Chat History	Retrieves recent conversation
6	Document Retriever	Searches knowledge sources
7	Prompt Template	Crafts context-rich prompts for the AI
8	Generator	Uses AI to generate responses
9	Chat Output	Sends generated response to the user

Benefits and Usefulness

Scalability: The workflow can handle many simultaneous customer queries without human intervention, allowing businesses to scale customer support effortlessly.
Automation: By automating information retrieval and response generation, the chatbot reduces manual workload and ensures consistency in support quality.
Context Awareness: By incorporating chat history and knowledge retrieval, the bot provides relevant, personalized, and up-to-date responses.
Easy Customization: The system message and prompt template can be easily modified to reflect company branding or policy changes.
Fallback Support: If the bot cannot answer a question, it proactively offers a support link, keeping the customer experience positive.

Use Cases

Customer support for e-commerce, SaaS, or service companies.
Answering FAQs and resolving common issues.
Providing guided onboarding for new users.
Scaling support during high-traffic periods.

This workflow empowers organizations to deliver responsive, accurate, and scalable customer service with minimal manual intervention, enhancing both efficiency and customer satisfaction.

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