Practical AI: Machine Learning, Data Science, LLM

Collaboration & evaluation for LLM apps

Jan 23, 2024

46:16

Snipd AI

The podcast discusses the challenges and opportunities of collaboration and evaluation in NLP models, emphasizing the significance of prompt engineering. It explores the collaboration between non-technical individuals and technical experts in AI applications. The chapter delves into the journey of managing versioning prompts and evaluating language model performance. It talks about building a collaborative tool for developers and non-technical users. The podcast also explores closed and open model ecosystems and the development of a question answering system through collaboration between domain experts and engineers. It highlights the exciting trends in AI and the vision of Humanloop becoming a proactive platform.

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Quick takeaways

Collaboration between non-technical prompt engineers and technical software engineers is crucial for building effective AI-driven apps.

Measuring performance in generative AI models is subjective, making evaluation and assessment challenging.

Deep dives

Overview of Human Loop and its Purpose

Human Loop is a platform that helps companies with prompt iteration, versioning, and management, as well as evaluation and monitoring of AI models. It provides a web app with an interactive playground-like environment where domain experts and engineers can collaborate. Domain experts can try different prompts, compare models, and save versions that they find effective. Engineers handle code orchestration, model calls, and setting up evaluation. The platform allows for different forms of evaluation, including unit tests, integration tests, and human evaluation. It also enables monitoring for performance and potential regressions.

Managing and collaborating on prompts as a critical artifact

00:42

Roles in Developing a Question Answering System

01:10

Empower Expertise: Bridging Domains with Direct Engagement

01:42

Prompt Engineering and Retrieval-Augmented Generation as Solutions

01:06

Introduction

5min

The Challenges of Managing Versioning and Evaluating Performance in Language Models

6min

Building a Collaborative Tool for Developers and Non-Technical Users

3min

Collaboration and Innovation in a Domain Expert and Technical Team Collaborative Environment

9min

Closed and open models and their impact on customer workflows

4min

Building a Human Loop System for Collaborating with Domain Experts in Question Answering

14min

Exciting Trends in AI and the Future of Humanloop

4min

Small changes in prompts can create large changes in the output behavior of generative AI models. Add to that the confusion around proper evaluation of LLM applications, and you have a recipe for confusion and frustration. Raza and the Humanloop team have been diving into these problems, and, in this episode, Raza helps us understand how non-technical prompt engineers can productively collaborate with technical software engineers while building AI-driven apps.

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Featuring:

Raza Habib – Twitter, LinkedIn
Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

Humanloop

Something missing or broken? PRs welcome!

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