Solution Accelerator
Large Language Models (LLMs) for Retail
Pre-built code, sample data and step-by-step instructions ready to go in a Databricks notebook
The practical impact of generative AI and large language models (LLMs) on society is growing by the day. Within the retail industry, practical applications for innovations such as ChatGPT and Dolly are widespread — including the rapid search of large product catalogs, streamlining customer service with intelligent chatbots, analyzing customer data and sentiments for personalization, and more — all with the goal of increasing customer satisfaction, loyalty and sales.
The fuel that makes an LLM run effectively is high-quality data, and lots of it. Databricks Lakehouse for Retail provides the underlying architecture and tooling to harness your customer and operational data to easily deliver generative AI and LLM models to your entire organization.
Deploying LLMs with Databricks: To use the latest Databricks technology for Retrieval Augmented Generation (RAG) in your GenAI applications, please reference the LLM Chatbot with RAG Demo
Enhancing product search
Large language models (LLMs) can be used to harness the rapidly growing range of content and goods to ensure customer searches yield the desired results. With the Databricks Lakehouse for Retail, organizations can:
Unify product, query and label data within a retailer’s product catalog
Enable rapid search with analytics against numerical arrays
Train and deploy an LLM model with Databricks Model Serving
Build an LLM-enabled chatbot
Use this Solution Accelerator to build a context-enabled LLM-based chatbot solution using content taken from our own knowledge base (made publicly available so that users can recreate our work).
The step-by-step code behind this work includes data preparation, agent development and deployment to a microservice that allows you to integrate the agent into any number of applications, and provides sufficient comments and documentation to help your organization understand the solution and get started with their own.
Automate product review summarization
Use this Solution Accelerator to streamline the summarization of customer feedback, allowing your organization to:
Process a high volume of reviews at a lower cost
Collect feedback from a wider range of products and summarize these on a regular basis
Task an LLM to extract different sets of information from each high-level category of reviews
Build Common Sense Product Recommendations
Use this Solution Accelerator to develop product recommendations based on common sense linkages for new-to-market products and optimized recommendation engines:
Convert all of our specific product descriptions and metadata into embeddings and store these in a searchable index
Task an LLM to recommend products based on their connection to other relevant products
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