There is an unending tsunami of news and announcements about new large-language models (LLMs) from large tech providers and the open source community. You're wondering: 

• Are open source LLMs as good as proprietary ones like GPT-4?
• What are the pros and cons of open source LLMs?
• How should I get started?

Prolego CEO, Kevin Dewalt, answers these questions in Episode 15 of our LLM & Generative AI Strategy Series and provides some practical advice. 

In this video, we performed a head-to-head comparison between the proprietary giant GPT-4 and an open-source darling called Phind-CodeLlama-34B-v2 (or let’s just stick with ‘Phind’) on the same Unified Natural Language Query problem we covered in Episode 4.

Here are the key takeaways:

1. GPT-4 vs. Open Source: GPT-4 is still the kingpin for generalized tasks, but open-source models like Phind aren't too far behind. They can be even more efficient in generating complex SQL queries.

2. Pros and Cons: With open source, you retain full data control and gain operational flexibility. This optionality comes at the expense of additional engineering effort. 

3. How to Start: Build a prototype with GPT-4 to check the viability. If things look promising, you can then switch gears to an open-source model for optimization—be it speed, cost, or data privacy.

These examples will help you accelerate your AI program by avoiding the most common mistakes in model selection.