The “build vs. buy” dilemma is a familiar challenge to software engineers, but with the rapid growth of AI and machine learning, it’s become even more nuanced. So, how should you approach this? We’ve got the perfect guest today to help you out!
Meet Jean-Louis Quéguiner, Founder and CEO of Gladia. Gladia specializes in building audio infrastructure for automatic speech recognition. Their APIs provide asynchronous and live-streaming capabilities for voice recognition in multiple languages. Over the course of his career, Jean-Louis has worked as a data engineer, a CTO, and with AI and quantum computing services, making him the ideal person for this topic.
In this episode, we tackle the “build vs. buy” question for LLMs. Jean-Louis shares key considerations to have in mind when architecting your LLM-based application, from understanding your specific use case to evaluating trade-offs between building custom solutions to leveraging existing APIs.
Listen now, and you’ll walk away with a wealth of practical advice to help you make the right choice for your business.
About Guest:
Name: Jean-Louis Quéguiner
What he does: Jean-Louis is the Founder & CEO of Gladia.
Company: Gladia
Where to find Jean-Louis: LinkedIn | X
Key Insights
⚡Voice is the biggest challenge for AI. Unlike text-based outputs, where small errors may go unnoticed, speech recognition has a much higher expectation of accuracy. Jean-Louis explains, “Nobody likes errors, and there’s nothing that looks more like a summary of ChatGPT than a summary of Claude. They all kind of look the same, even if they are not using exactly the same words, globally, means the same thing. The problem you have with speech recognition is nobody’s going to be tolerant with you if you make a mistake on your address, on the names, first name, last name, phone numbers, etc. So you need to be perfect. And perfection is extremely hard when it comes to AI, especially if you want to have extremely low latency.”
⚡Match the LLM model to your specific needs. There’s no one-size-that-fits-all LLM model. The best choice depends on the specific requirements of your application. Jean-Louis shares some key considerations to have in mind: “It’s always about those kind of questions that you need, which is what is your end goal? What is your use case? What are the conditions the system is running? What’s quality you have able to accept? Define what is acceptable as a metric, and then you’re going to be able to choose your model.”
⚡Keep testing different LLMs to see what works best. There’s no substitute for real-world data, so you always need to keep testing and trying out different LLM models and versions. Jean-Louis says, “I think it’s hard to say now that you have a proper methodology. I think the only thing is to test, try, look at the leaderboards again, amazing LLM leaderboards and try to make these metrics that give a sense of what is good quality. But I think in the end […] it all comes to being in the real world situation. Try x, y, z and see how people are using it.”