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:
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.