Challenge: Keeping track of context across multiple turns of conversation.
Solution: Implement a context management system that tracks entities, topics, and previous queries. Use this context to enhance subsequent queries.
Create intelligent chatbots that can answer questions about your data
Chatbots and virtual assistants powered by Infactory can answer specific questions about your data accurately and efficiently. This guide explains how to build chatbots that leverage Infactory’s data intelligence capabilities.
Traditional AI-powered chatbots face several challenges when answering data-specific questions:
LLMs can make up false information when they don’t know the answer
Answers to the same question may vary between sessions
Each query requires full LLM processing, which can be slow
Requires complex prompt engineering and fine-tuning
Infactory solves these challenges by:
User asks a question
The user enters a natural language question in your chatbot interface.
Question sent to your backend
Your application sends the question to your backend server.
Backend calls Infactory API
Your backend server forwards the question to Infactory’s unified API endpoint.
Infactory processes the question
Infactory selects the appropriate query, extracts parameters, and executes it against your data.
Return structured data response
Infactory returns a structured data response to your backend.
Format and display the answer
Your application formats the structured data into a natural language response and displays it to the user.
The simplest implementation directly maps user questions to Infactory responses:
For a more conversational experience, you can combine Infactory with an LLM:
In this approach:
To handle follow-up questions, maintain conversation context and reference previous responses:
Enhance your chatbot with visual representations of data:
To handle complex conversations with multiple turns:
As usage grows, consider these scaling strategies:
Distribute API calls across multiple backend instances
Cache common queries to reduce API calls and improve response times
Use a message queue for handling high volumes of requests
Deploy as serverless functions that scale automatically with demand
Track your chatbot’s performance:
Define what types of questions your chatbot should answer
Have graceful responses for questions outside your query coverage
Start simple and add complexity as you learn user patterns
Regularly analyze unanswerable questions to identify new queries to create
Allow users to flag incorrect or unhelpful answers
Validate with actual users rather than just hypothetical questions
Handling Ambiguous Questions
Challenge: Users ask questions that could match multiple queries or have unclear parameters.
Solution: Implement a clarification flow where the chatbot asks follow-up questions when a question is ambiguous. For example:
Maintaining Conversation Context
Challenge: Keeping track of context across multiple turns of conversation.
Solution: Implement a context management system that tracks entities, topics, and previous queries. Use this context to enhance subsequent queries.
Unrealistic User Expectations
Challenge: Users expect the chatbot to answer any question, regardless of whether the data supports it.
Solution: Set clear expectations and provide informative responses about the chatbot’s capabilities:
Answer questions about order status, shipping times, and product availability
Help employees explore business metrics without needing SQL knowledge
Provide conversational access to sales performance data
Recommend products based on customer data and preferences
After building your chatbot, consider:
Create intelligent chatbots that can answer questions about your data
Chatbots and virtual assistants powered by Infactory can answer specific questions about your data accurately and efficiently. This guide explains how to build chatbots that leverage Infactory’s data intelligence capabilities.
Traditional AI-powered chatbots face several challenges when answering data-specific questions:
LLMs can make up false information when they don’t know the answer
Answers to the same question may vary between sessions
Each query requires full LLM processing, which can be slow
Requires complex prompt engineering and fine-tuning
Infactory solves these challenges by:
User asks a question
The user enters a natural language question in your chatbot interface.
Question sent to your backend
Your application sends the question to your backend server.
Backend calls Infactory API
Your backend server forwards the question to Infactory’s unified API endpoint.
Infactory processes the question
Infactory selects the appropriate query, extracts parameters, and executes it against your data.
Return structured data response
Infactory returns a structured data response to your backend.
Format and display the answer
Your application formats the structured data into a natural language response and displays it to the user.
The simplest implementation directly maps user questions to Infactory responses:
For a more conversational experience, you can combine Infactory with an LLM:
In this approach:
To handle follow-up questions, maintain conversation context and reference previous responses:
Enhance your chatbot with visual representations of data:
To handle complex conversations with multiple turns:
As usage grows, consider these scaling strategies:
Distribute API calls across multiple backend instances
Cache common queries to reduce API calls and improve response times
Use a message queue for handling high volumes of requests
Deploy as serverless functions that scale automatically with demand
Track your chatbot’s performance:
Define what types of questions your chatbot should answer
Have graceful responses for questions outside your query coverage
Start simple and add complexity as you learn user patterns
Regularly analyze unanswerable questions to identify new queries to create
Allow users to flag incorrect or unhelpful answers
Validate with actual users rather than just hypothetical questions
Handling Ambiguous Questions
Challenge: Users ask questions that could match multiple queries or have unclear parameters.
Solution: Implement a clarification flow where the chatbot asks follow-up questions when a question is ambiguous. For example:
Maintaining Conversation Context
Challenge: Keeping track of context across multiple turns of conversation.
Solution: Implement a context management system that tracks entities, topics, and previous queries. Use this context to enhance subsequent queries.
Unrealistic User Expectations
Challenge: Users expect the chatbot to answer any question, regardless of whether the data supports it.
Solution: Set clear expectations and provide informative responses about the chatbot’s capabilities:
Answer questions about order status, shipping times, and product availability
Help employees explore business metrics without needing SQL knowledge
Provide conversational access to sales performance data
Recommend products based on customer data and preferences
After building your chatbot, consider: