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From Rider Names to Support Tickets: How Uber’s AI Keeps It Together
Data Science

From Rider Names to Support Tickets: How Uber’s AI Keeps It Together

Uber’s New Prompt Engineering Toolkit makes it easy to create, test, and use prompts from different models

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Data Tinkerer
Dec 12, 2024
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From Rider Names to Support Tickets: How Uber’s AI Keeps It Together
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Overview of Uber’s Prompt Engineering Toolkit (Source: Uber)

TL;DR

Situation: Uber needed a centralized system to efficiently create, manage, and deploy prompts from LLMs

Task: Develop a toolkit that streamlines the prompt engineering lifecycle, including exploration, iteration, evaluation, and productionization of prompt templates

Action: Uber built the Prompt Engineering Toolkit, featuring a model catalog, GenAI Playground, auto-prompting capabilities, version control, and evaluation tools to facilitate rapid iteration and responsible AI usage

Result: The toolkit enabled faster prompt development, reuse of existing prompt templates, and enhanced monitoring of LLM performance in production

Use Cases: User Data Validation, Support Ticket Summarization

Tech Stack/Framework: LLM, LangChain, RAG

Explained Further

Key Features:

  • Model Catalog: A comprehensive repository detailing available LLMs, including specifications, use cases, cost estimates, and performance metrics.

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