Leveraging Large Language Models in your Software Applications
<p><em>How can you leverage the capabilities of Large Language Models (LLMs) within your software applications?</em></p>
<p>You cannot simply create a thin application layer above an LLM API. Instead you need to design and build a number of components to ‘tame’ the underlying models and also to differentiate your product.</p>
<p>Over the last few months, a number of techniques and approaches have emerged on how to ground LLMs to your application’s use cases, data and user sessions. Other techniques can be used to maintain memories and states of previous interactions with the user, or to breakdown objectives into smaller tasks and subtasks.</p>
<p>This is not a detailed technical guide on how to implement and execute these techniques. Instead, I try to explain how these different components can be combined into an architecture to build AI software on top of LLMs. To help demonstrate, I also use a fictitious ‘fitness application’ as an example.</p>
<p><a href="https://medium.com/@simon_attard/leveraging-large-language-models-in-your-software-applications-9ea520fb2f34">Website</a></p>