Large Language Model (LLM) Stack — Version 5

In my attempt to interpret what is taking place and where the market is moving, I created a product taxonomy defining the various LLM implementations and use cases.

There are sure to be overlaps between some products and categories listed above. I looked into the functionality of each and every product listed, hence the categories & segmentation of the landscape is a result of that research.

There are a few market shifts and trends taking place:

• Diversification of LLM models are continuing more open-source options being available. I created a new category for LLM Playgrounds, and there is a significant opportunity to give makers access to models to experiment with.
• Default LLM functionality is continuing to expand, with no-code fine-tuning dashboards and fine-tuning being available for more models.
• Basic LLM offerings are superseding existing products. Consider here the growing LLM context windows, applications needing to reference multiple models and more.
• Considering the image above, I would argue products higher up in the stack are more vulnerable.
• LLMs are releasing end-user chat interfaces, like HuggingChat, ChatGPT and Coral from Cohere.
• Prompt Engineering techniques are growing and the flexibility and utility of LLMs are really coming to the fore.
• LLM fine-tuning is still relevant to create industry and market related models, for instance medical, legal, engineering, etc.
• However, for general implementations a popular architecture is one of RAG, where accurate and contextually relevant reference data is included in the prompt to increase LLM response accuracy and negates hallucination.

⭐️ Follow me on LinkedIn for updates on Large Language Models ⭐️

I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.

LinkedIn

Get an email whenever Cobus Greyling publishes.