Leveraging Large Language Models (LLMs) for Product Catalog Summarization at a Large Convenience Store Chain

Client Overview
A global convenience store chain with thousands of locations across multiple countries needed an efficient way to manage and present its extensive product catalog. The catalog contained thousands of SKUs, spanning multiple categories, with complex and often redundant product descriptions from different suppliers.

Challenges

1. Data Overload: The sheer volume of product descriptions made it difficult for customers and store associates to quickly understand key product attributes.
2. Inconsistent Formatting: Supplier-provided descriptions varied in length, terminology, and quality.
3. Manual Effort: The existing approach relied on manual curation and editing, making it time-consuming and costly.
4. Search Optimization: Long and unstructured descriptions affected searchability and discoverability of products in both online and in-store systems.

Solution: Implementing LLM-Based Summarization
The convenience store chain adopted a Large Language Model (LLM) to automatically generate concise, structured, and uniform product summaries. The solution involved:

• Data Ingestion & Preprocessing: Extracting raw descriptions from various sources and normalizing them.
• LLM Fine-Tuning & Prompt Engineering: Training and optimizing the model to ensure summaries retain essential product attributes (e.g., size, brand, key features).
• Automated Summarization: Generating consistent, high-quality product summaries in real time.
• Quality Assurance & Human-in-the-Loop Review: Deploying a hybrid approach where high-confidence summaries were auto-approved while ambiguous cases were reviewed by product managers.

Implementation Details

• Platform: The solution was deployed using a cloud-based NLP service integrated with the company’s data pipeline.
• Technology Stack: Databricks for data processing, Azure OpenAI GPT models for summarization, and an API layer for real-time updates.
• Integration: The summarized data was integrated with the company’s e-commerce platform and in-store product lookup system.

Results

1. Increased Efficiency: Reduced manual effort in product catalog management by 80%.
2. Improved Search & Discovery: Enhanced search relevance and improved product discoverability.
3. Consistent Customer Experience: Standardized and easy-to-understand product descriptions across all channels.
4. Scalability: The system handled thousands of new SKUs each month without additional operational burden.