Choosing LLMs for Your Products: What’s Most Important to You?
Natalia Demianenko
9 replies
If you’re incorporating LLMs into your products, how do you decide which one to use? Which of these factors is most crucial for you, and are there any other considerations you take into account?
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Future4 Coding@future4coding
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For me, quality is the top factor. It’s essential that the LLM delivers accurate and reliable responses. Price and customization are secondary considerations.
@isabella_wandrei Absolutely, quality is crucial. An LLM that delivers reliable responses is a must, and I agree that price and customization are important but secondary
I prioritize ease of integration. I want an LLM that fits smoothly into my existing systems without causing disruptions. Support and documentation are also crucial for smooth implementation.
@sandy_leventh Ease of integration is a great point. It's important to have an LLM that works seamlessly within your systems, and having good support and documentation makes a huge difference. But it depends on the task
Accuracy is definitely the top factor for me too. If the responses aren't reliable, it defeats the whole purpose of using an LLM. Customization options are nice to have for tailoring the outputs to your specific use case. And of course price matters, but I'd prioritize quality over saving a few bucks. Curious what LLM providers people have had the best experience with on those fronts?
@nancywright I agree - accuracy is paramount when choosing an LLM. Customization and quality definitely come first, even if it means spending a bit more. I'm also curious about which providers others have had the best experience with
For sure latency and reliability matter a ton. Like if the LLM is super high quality but takes forever to respond or goes down all the time, that's a no-go. Customization is key too - being able to fine-tune the model on your specific use case and data. Price matters but I'd pay more for better performance. Support and ease of integration are important considerations too.
Customization is key for me. Being able to fine-tune the LLM on my specific domain and use case is critical to getting high quality, relevant outputs tailored to my product's needs. Of course accuracy and reliability matter too, but without customization, even a high quality generic model may not be the best fit.