The Large Language Model Landscape

The number of commercial and open LLM providers has exploded in the last 2 years, and there are now many options to choose from for all types of language tasks. And while the main way of interacting with LLMs is still via APIs and rudimentary Playgrounds, I expect that an ecosystem of tooling that helps accelerate their wide adoption will be a growing market in the near future.


  • Large Language Models (LLMs) functionality can be segmented into five areas: Knowledge Answering, Translation, Text Generation, Response Generation and Classification.
  • Classification is arguably the most important to today’s enterprise needs, and text generation the most impressive and versatile.
  • The commercial offerings and more general offerings are Cohere, GooseAI, OpenAI and AI21labs. GooseAI currently only focuses on generation.
  • The open-source offerings are Sphere, NLLB, Blender Bot, DialoGPT, GODEL and BLOOM.
  • The tooling ecosystem is still in a nascent state with many areas of opportunity.

LLM Functionality

Response Generation
Text Generation
Knowledge Answering


Cohere, OpenAI, AI21labs, GooseAI, Blender Bot, DialoGPT, GODEL, BLOOM, NLLB, Sphere

Tooling Ecosystem

Data-centric Tooling, Playgrounds, Notebooks, Prompt Engineering Tools, Hosting

LLMs & Playgrounds

LLMs are accessed as APIs, so the barebones tooling required to make use of their APIs is the command-line, a development environment or Jupyter Notebooks; Cohere is doing a really great job of pushing out content that shows how to apply LLMs to real-life use-cases with simple scripts and integrations.

Vendors also clearly realise that to make experimenting and adopting LLMs easier, they need to provide no-code environments in the form of Playgrounds that expose the different tasks and tuning options: these are a great starting point to understand what can be achieved.

The GooseAI playground view, with tuning options on the right.

Data-Centric Tooling

I'm anxious to see LLMs more deeply integrated within the "core" workflows required to develop conversational AI and other use-cases like analytics etc; it seems clear that LLM APIs and their embedding spaces are positioned to unlock more powerful:

  • Semantic search (useful to explore unstructured data)
  • Clustering (needed to identify topics of conversations or intents)
  • Entity extraction (via text generation)
  • Classification (either via few-shot learning examples, or fine-tuning the actual models)


Finally, LLMs are massive models, and they are expensive and difficult to run.

In Conclusion

LLMs are not chatbot development frameworks, and the one should not be compared to the other. There are specific LLM use-cases in conversational AI, and chatbot and voicebot implementations can definitely benefit from leveraging LLMs.



Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; NLP/NLU/LLM, Chat/Voicebots, CCAI.

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Cobus Greyling

Chief Evangelist @ HumanFirst. I explore and write about all things at the intersection of AI and language; NLP/NLU/LLM, Chat/Voicebots, CCAI.