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Appen + Databricks: Using RAG to Build a Better Credit Card Chatbot

Published on
May 1, 2024

In an era where AI chatbot solutions are transforming customer support, high-quality, precisely tailored AI solutions are paramount. However, data quality and integration issues, along with the resource-intensive nature of large language models (LLMs), often hinder large-scale projects by causing delays and scalability challenges.

Appen's AI Data Platform (ADAP) provides high-quality, well-annotated data and scalable solutions that streamline the training process and enhance model performance. This ensures faster time-to-market and more effective LLM deployments for enterprises.

A recent demonstration by Appen's Mike Davie and Roger Sundararaj showcased the power of integrating ADAP with Databricks to optimize a credit card customer support chatbot and dramatically elevate its accuracy, relevance, and performance.

Demo in Action

The demo centers around a use case for a credit card company, illustrating the process from data preparation to endpoint testing. Data pertaining to various credit cards is vectorized and ingested into a Retrieval Augmented Generation (RAG) endpoint using Databricks. This allows the chatbot to fetch relevant information swiftly and generate responses that are not only accurate but also contextually appropriate.

The endpoint, tested with questions about specific credit cards, demonstrates its capability to deliver detailed and relevant content, simulating a knowledgeable sales representative. This functionality highlights how Appen's ADAP seamlessly integrates with technologies like Databricks, enhancing the model's ability to handle complex queries with ease.

Quality, Integration & Scalability

Appen's platform excels at large-scale projects and seamlessly integrates with existing enterprise systems, like Databricks, enhancing adaptability and sustainability. This is particularly crucial for enterprises developing precise and relevant LLMs with high-quality internal data.

As the demo emphasizes, Appen improves the efficiency of existing data management tools, facilitating better AI model training without the need to relocate data – and provides clear insights into which configuration yields results. These insights are vital for enterprises aiming to optimize their AI solutions based on real-world application and feedback.

Appen is Ideal for Enterprise LLMs

For any large enterprise looking to train their critical LLMs, Appen is the right choice. The platform not only streamlines the AI model lifecycle with its comprehensive data handling capabilities—from ingestion and preparation to evaluation and feedback—but also supports ongoing optimization through A/B testing and performance benchmarking.

Transform Your AI Strategy

Contact Appen today to learn how our expertise and advanced platforms can help accelerate your AI journey.

By harnessing the power of high-quality, scalable data solutions and expert-driven platforms, enterprises can ensure their generative AI applications perform exceptionally well, enhancing both customer satisfaction and operational efficiency.

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May 01, 2024