Implementing Retrieval-Augmented Generation (RAG) for Let’s Play Soccer
Client Introduction
Let’s Play Soccer (LPS) is a chain of indoor soccer facilities that provides field rentals for teams and individuals. With multiple locations and varying rental rates depending on time, day, and facility, LPS receives frequent customer inquiries about pricing and availability.
Problem/Client Challenges
- Customers often submit unstructured requests (e.g., “I want to rent a field”) with incomplete grammar or missing details.
- Traditional Large Language Models (LLMs) like GPT-4, when queried directly, produced rambling or hallucinated responses that were not precise enough for customer-facing applications.
- LPS needed a way to leverage its internal pricing data without building or training a costly multi-billion parameter LLM from scratch.
The challenge: How can AI accurately answer customer inquiries using proprietary data?
Solution
AllCode implemented a RAG workflow:
- Data Preparation
- Extracted proprietary field rental rates from LPS’s internal database.
- Stored records in a vector database, indexed by semantic embeddings (e.g., “renting a field at Timpanogos”).
- Application Integration
- Incoming customer prompts were converted into vector representations.
- Queried the vector database to retrieve semantically similar records (e.g., facility, day, time, rate).
- Re-ranked results based on metadata such as Facility ID, day of week, and time of day.
- Augmented Prompting
- Combined the retrieved field rental data with the original customer query.
- Passed this augmented prompt to GPT-4, enabling the model to generate accurate, context-aware responses.
Results
- Improved Accuracy: GPT-4 responses became precise, directly quoting actual rental rates (e.g., “Field 1 at 6am on weekdays costs $50 per hour”).
- Customer-Friendly Output: Responses included clear breakdowns of weekly/bi-weekly costs and booking instructions.
- Reduced Hallucinations: By grounding GPT-4 with internal data, the model avoided vague or incorrect answers.
- Cost Efficiency: LPS avoided the expense of training a custom LLM, instead leveraging RAG to integrate existing data sources.
- Enhanced Customer Experience: Customers received quick, reliable, and actionable information, improving satisfaction and reducing manual support workload.
Conclusion
With AllCode’s expertise in cloud architecture and AI integration, Let’s Play Soccer successfully implemented Retrieval-Augmented Generation (RAG) to bridge the gap between customer inquiries and proprietary data. This approach demonstrates how startups and SMBs can cost-effectively enhance AI applications without building new models, while delivering context-aware, accurate, and customer-worthy responses.