Making Email Better With AI At Shortwave
Data Engineering Podcast
Navigating Technical Challenges in AI-Powered Product Development
Exploring the complexities of testing AI features with limited datasets, designing internal data extraction agents, and the need for enhanced tools for handling LLM calls in software applications.
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Summary
Generative AI has rapidly transformed everything in the technology sector. When Andrew Lee started work on Shortwave he was focused on making email more productive. When AI started gaining adoption he realized that he had even more potential for a transformative experience. In this episode he shares the technical challenges that he and his team have overcome in integrating AI into their product, as well as the benefits and features that it provides to their customers.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Andrew Lee about his work on Shortwave, an AI powered email client
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Shortwave is and the story behind it?
- What is the core problem that you are addressing with Shortwave?
- Email has been a central part of communication and business productivity for decades now. What are the overall themes that continue to be problematic?
- What are the strengths that email maintains as a protocol and ecosystem?
- From a product perspective, what are the data challenges that are posed by email?
- Can you describe how you have architected the Shortwave platform?
- How have the design and goals of the product changed since you started it?
- What are the ways that the advent and evolution of language models have influenced your product roadmap?
- How do you manage the personalization of the AI functionality in your system for each user/team?
- For users and teams who are using Shortwave, how does it change their workflow and communication patterns?
- Can you describe how I would use Shortwave for managing the workflow of evaluating, planning, and promoting my podcast episodes?
- What are the most interesting, innovative, or unexpected ways that you have seen Shortwave used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Shortwave?
- When is Shortwave the wrong choice?
- What do you have planned for the future of Shortwave?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
Links
- Shortwave
- Firebase
- Google Inbox
- Hey
- Superhuman
- Pinecone
- Elastic
- Hybrid Search
- Semantic Search
- Mistral
- GPT 3.5
- IMAP
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
- Dagster:  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage. Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free!
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