Building an advanced Golf Instruction Chatbot

The source code is hosted on GitHub, found here.

As a software engineer with a passion for both AI and golf, I recently completed a project that combines these interests in an exciting way. I developed a sophisticated Retrieval-Augmented Generation (RAG) chatbot that provides golf instruction in the style of the legendary golfer and instructor, Ben Hogan. This project not only challenged my technical skills but also pushed the boundaries of what’s possible with multimodal AI in a specialized domain.

The Vision

The goal was to create an AI assistant that could offer personalized golf instruction, complete with relevant visual aids, mimicking the teaching style of Ben Hogan. This required combining cutting-edge natural language processing with advanced computer vision techniques to create a truly multimodal experience.

Technical Challenges and Solutions

Intelligent Text Processing

One of the first challenges was to process the textual golf instruction content in a way that preserved context and meaning. I implemented the StatisticalChunker from the semantic_chunkers library, which allowed for intelligent text segmentation. This approach ensured that when the chatbot retrieved information, it had access to coherent, context-rich chunks of text rather than arbitrary segments.

AI-Powered Image Labeling

A key innovation in this project was the use of Claude, an advanced AI model, to generate detailed descriptions of golf-related images. This step was crucial in bridging the gap between textual and visual content. By having AI-generated labels, we could more effectively match relevant images to the chatbot’s text responses.

Multimodal Embeddings with CLIP

To create a unified representation of both text and images, I utilized CLIP (Contrastive Language-Image Pre-Training). This allowed the system to understand the relationship between textual descriptions and visual content, enabling more accurate and relevant responses.

Efficient Vector Storage and Retrieval

For storing and retrieving the embeddings, I used a combination of Chroma for text embeddings and FAISS for image embeddings. This dual approach allowed for fast and efficient similarity searches across both modalities.

The RAG Pipeline

The heart of the system is the Retrieval-Augmented Generation pipeline, built using LangChain. This pipeline retrieves relevant context based on user queries and then uses OpenAI’s GPT model to generate responses that sound authentically like Ben Hogan’s teaching style.

Prompt Engineering

Crafting the right prompts was crucial for maintaining the persona of Ben Hogan and ensuring that the AI could effectively incorporate diagram descriptions into its responses. This required multiple iterations and fine-tuning to get the tone and content just right.

The User Experience

To make the chatbot accessible and user-friendly, I developed a Streamlit web application. Users can input their golf-related questions and receive real-time responses along with relevant visual aids. The interface is designed to be intuitive, allowing golfers of all levels to benefit from the AI-powered instruction.

Ethical Considerations and Future Directions

While building this project, I was acutely aware of the ethical implications of using copyrighted material. The current version uses content derived from published golf instruction materials for educational and research purposes only. Moving forward, I plan to explore ways to develop original content in collaboration with golf professionals or use open-source materials to ensure full compliance with copyright laws.

Future enhancements I’m considering include:

1. Implementing conversation history management for more context-aware interactions.
2. Expanding the knowledge base to cover a wider range of golf topics.
3. Optimizing the retrieval and generation pipeline for faster response times.
4. Adding a user feedback mechanism for continuous improvement.
5. Exploring the possibility of fine-tuning language models on golf-specific data.

Conclusion

This project was a fascinating exploration of how AI can be applied to niche domains like golf instruction. By combining advanced NLP techniques, computer vision, and careful prompt engineering, we can create AI assistants that not only provide accurate information but do so in a way that captures the essence of a specific teaching style.

The experience has deepened my understanding of multimodal AI systems and reinforced the importance of considering ethical implications in AI development. As AI continues to evolve, projects like this highlight the potential for creating highly specialized, intelligent assistants that can enhance learning experiences across various fields.

I’m excited to continue refining this golf instruction chatbot and exploring new applications of multimodal AI in other specialized domains. The intersection of AI and human expertise offers endless possibilities for innovation, and I’m thrilled to be part of this journey.