1) Large Language Models
a) LLM as a service.- where vendor offers LLM as an API on their infrastructure
b) Tools necessary for LLM Stack:
These are categories of tools necessary for fine tuning and deploying proprietary solutions built on top of open-source models
2) Prompt Engineering Tools
Main idea will be to promote in-context learning instead of fine tuning at lower costs and without using sensitive data
a) Vector Databases retrieve contextually relevant information for certain prompts.
b) Prompt Execution enables optimizing and improving the model output based on managing prompt templates to building chain-like sequences of relevant prompts.
c) Prompt Logging, Testing, and Analytics … Let’s just say it’s an emerging space that has no categories yet.
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