E5: Reliable Software Engineering & LLMs with Adam Azzam
Sep 25, 2023
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Adam Azzam, AI Product Lead at Prefect, discusses challenges of merging traditional software engineering with AI engineering. They explore marrying large language models with strongly typed semantic interfaces, suitable problems for Marvin AI, and the benefits of using LLMs in software engineering.
Integrating LOMs with classical software engineering presents challenges due to differences in communication and options for working with LOMs are limited, which motivated the development of the open-source project Marvin.
Ambient AI aims to seamlessly integrate LOMs into existing software development practices, combining LOM technology with established best practices to enhance and complement existing software.
Deep dives
Working with LOMs can be challenging due to integration difficulties
The podcast episode discusses the challenges that developers face when working with LOMs (Language Output Models) in classical software. Integrating LOMs, which have a natural language interface, with structured software can be difficult due to the differences in how they communicate. The speaker highlights the limited options available for working with LOMs, such as using pre-packaged agents or hyper-specific libraries for specific tasks. However, the discussion revolves around the need to find a solution that combines the advantages of LOMs, such as their ability to understand nuanced computing tasks, with the structured and strongly typed nature of classical software. This challenge serves as the motivation behind the development of the open-source project called Marvin.
The concept of ambient AI simplifies the developer experience
The podcast explores the concept of ambient AI, which aims to make working with LOMs feel seamless and natural for developers. Ambient AI refers to integrating LOMs into the existing software development practices and design patterns, rather than replacing them entirely. The goal is to combine the advances in LOM technology with the established best practices in software development. This union allows LOMs to enhance and complement existing software, providing additional functionalities and supercharging the developer's workflow. By incorporating LOMs as a background process that adds to the classical code, ambient AI bridges the gap between the current state of software development (referred to as software 2.0) and the potential future advancements (software 3.0).
Marvin's applications include zero-shot classification and entity extraction
The podcast episode mentions several applications of Marvin, including zero-shot classification, entity extraction, and business logic generation. Marvin simplifies the process of zero-shot classification, in which developers can classify content into different categories without explicitly training the LOM. It also facilitates entity extraction from unstructured data, allowing developers to easily extract structured information such as names, dates, and locations from text. Additionally, Marvin enables the generation of synthetic data by leveraging LOMs. For example, it can create more realistic sales demos for enterprises by mock generating data that aligns with potential customer requirements. These applications demonstrate how Marvin empowers developers to harness the power of LOMs in various problem-solving scenarios.
Marvin's developer-centric approach and future directions
Marvin takes a developer-centric approach by focusing on giving developers control and flexibility. It provides a fixed set of contracts and primitives that developers can use to build their solutions effectively. Rather than trying to solve all aspects of problem-solving, Marvin emphasizes collaborating with developers, enabling them to leverage external resources and integrate them with Marvin's core components. This approach ensures that developer teams can align their efforts, build reliable software, and simplify complex business logic using LOMs. As Marvin evolves, it plans to address challenges related to persistence, state management, and expanding the possibilities for developers as they work with increasingly complex business logic and larger datasets.
We chatted with Adam Azzam, PhD - AI Product Lead at Prefect - about the challenges of merging traditional software engineering with AI engineering. We go over why integrating LLMs present such a challenge to many developers and how Marvin AI - a new open source tool - can help.
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