Replit AI Podcast

02: Unleashing LLMs in Production: Challenges and Opportunities with Chip Huyen

May 24, 2023

58:44

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Quick takeaways

Large language models present challenges and opportunities in the industry.

Transition from task-specific to more general models requires human evaluation for user preferences.

Deep dives

Discussing Large Language Models in Production

Large language models offer challenges in industry, but also tremendous opportunities. Companies like Replied are navigating decisions on using pre-trained models or building bespoke models for specific use cases.

Introduction

2min

The Challenges of Deploying LLMs

3min

The Internal Working of LLMs

2min

The Slow Ramp Up of Open Source Modeling

4min

The Challenges of Building a BSPOC Model

3min

The Importance of Continuous Learning in Clay Pots

2min

The Future of LMS

2min

The Wrong Approach to Context Learning

5min

How to Make Context Learning More Effective

4min

10.

The Importance of Large Language Models

4min

11.

Clay Pots: The Future of Embedding

2min

12.

The Use of Embeddings in Recommendation Systems

5min

13.

The Pros and Cons of Using API in Production

5min

14.

Open Source vs. B-Aid API: Which Is Better?

2min

15.

Human Eval: A Benchmark for Open Source

5min

16.

The Evolution of Chatbots

6min

17.

Open Source vs. API: What You Need to Know

3min

In this second installment of AI panels at Replit, Michele, Chip, and Amjad discuss putting Large Language Models (LLMs) into production. The challenges and opportunities offered by LLMs are considered, as well as their potential impact on the industry.

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Replit AI Podcast

02: Unleashing LLMs in Production: Challenges and Opportunities with Chip Huyen

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Discussing Large Language Models in Production

Experience with Training and Deploying Bespoke Models

The Evolution of LLMs and Benchmarking Challenges

Remember Everything You Learn from Podcasts