If you’ve been trying to keep up with AI, there’s a good chance you’ve come across the term RAG. When I first learned heard the term, I wasn’t exactly sure what it meant form the context of the discussion.
Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy of large language models (LLMs) by giving them a wider context window and including external (from the LLM’s database) knowledge sources.
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Agentic AI is essentially giving AI a specific job that it will execute on a regular basis and giving it the autonomy to do so.
There are five main categories of agentic AI today:
The most basic agentic AI models are reactive and are built to analyze an input from the environment then use condition based logic/rules to make a decision on what to do next. For instance, your thermostat has a sensor that evaluates the temperature of the air (environmental input) and checks it against the conditional rules (if the temp is above 72 degrees F, then turn on cooling until it reaches 72 degrees). Once it evaluates the data against the conditional logic, it performs an action.