AI business chatbot project
My Projects

The AI Business chatbot project

Project Report: Business Chatbot Assistant
Date: September 21-22, 2024
Duration: 4 hours

1. Project Overview

My AI Business Chatbot project focused on creating an AI-powered chatbot to assist business owners in learning detailed information about their operations and micro-managing various aspects of their businesses using their own databases. The chatbot is designed to understand complex user prompts, query the underlying database, and return optimized, human-readable results.

2. Key Features of AI Business Chatbot project

  • Custom Database Training: We trained the chatbot with a custom database relevant to the business. This involved fine-tuning a large language model (LLM) to recognize specific business-related terms and respond to queries accordingly.
  • Contextual Understanding and Response Generation: The chatbot uses a combination of LLM and Retrieval-Augmented Generation (RAG) techniques to enhance the accuracy of responses. RAG helps by integrating additional context during each interaction, making the responses more precise and tailored to the user’s data.
  • Text-to-SQL Translation: To process user queries, we employed a text-to-SQL model like T5 or BERT. This allowed the chatbot to convert natural language questions (e.g., “What product do we need to buy more of?”) into structured SQL queries that retrieve the relevant data from the database.
  • Prompt Simplification and Paraphrasing: Given that user prompts can be ambiguous or complex, we added a layer to simplify and translate these prompts into queries the model could process. The chatbot also paraphrases the returned data into user-friendly language to enhance usability.

3. Challenges Encountered

  • Fine-Tuning Complexity: Fine-tuning the model with the user’s custom dataset presented challenges such as managing high training costs and ensuring up-to-date information. Additionally, gaining insight into the model’s decision-making process was difficult, making troubleshooting harder.
  • Ambiguity in User Prompts: Translating ambiguous or vague user prompts into queries was a complex task. We tackled this by integrating a prompt-simplification layer to streamline and clarify requests.
  • User-Friendly Output: Ensuring that the returned data was easily understandable required a post-processing step to paraphrase query results into natural, human-readable sentences.

4. Results for AI Business Chatbot Project

The chatbot successfully demonstrated the ability to:

  • Understand and process complex business-related queries.
  • Provide detailed and actionable insights using data from the business database.
  • Improve user interaction with clear, simplified, and paraphrased responses.

5. Conclusion

This project has successfully laid the foundation for a powerful business assistant chatbot. By combining LLM fine-tuning, RAG, and text-to-SQL translation, the chatbot can assist business owners in gaining meaningful insights and managing their businesses more effectively.

AI Business Chatbot Project