Exploring some toolings that enable developers to build and deploy LLM-based applications.

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Large language models are new and are marked by their continuous and accelerated growth, with groundbreaking advancements and innovations emerging at a pace that people trying to catch up often slip and fall behind. It is important to highlight some of the toolings that are available in the LLM ecosystem that provide the functionality that users want because it is often assumed or perceived that the model is the application and provides all the capabilities that users want.

The figure above was created by Andreessen Horowitz, and in a post titled Emerging Architectures for LLM Applications, they showed, in their word, “most common systems, tools, and design patterns we’ve seen used by AI startups and sophisticated tech companies. They also mentioned that the app stack may change substantially because the LLM space is fast evolving.

This app stack is based on in-context learning. In-context learning entails taking a model with general language understanding and refining its expertise in a targeted field. In the case of the LLM, this involves adjusting its language patterns, recognizing domain-specific terminology, and honing its ability to comprehend and generate content relevant…