The future of LLM integration – plugins, agents, assistants, GPTs, and multimodal models

So far, we have explored integrating LLMs using templates, no-code tools, and developer platforms such as LangChain. However, innovation in this space is rapid. We can expect even more powerful and flexible integration capabilities to emerge. Here are some key developments on the horizon:

• LLM plugins and extensions

  Rather than custom coding, expect to see plugins and extensions for directly integrating LLMs into common platforms. We already have plugins for ChapGPT Plus and some of them, such as Code Interpreter, are extremely powerful. Code Interpreter provides a sandboxed Python environment for executing code. While designed for programmers, it can also empower general users to accomplish many tasks.

  For example, you can leverage Code Interpreter to convert PDFs using OCR, edit video files, solve math problems, conduct data analysis and visualization, generate graphs and charts,...