Blogs
Some of the earlier posts in this series talked about AI assistants. These AI Assistants use their training (and sometimes extra data) to answer questions, summarise information, and do small tasks. They are good at producing human-like responses, but they are not truly independent. Usually they follow a simple loop: take input, process it, then produce output.
Agents are the next step. Instead of just answering, an agent can make decisions, use external tools, act on its environment, and check whether those actions worked i.e more autonomous. If something does not go well, it can re-iterate and improve the outcome.
Retrieval Augmented Generation aka RAG is a new paradigm in the world of Generative AI which allows AI systems to provide more contextual, accurate and personalised responses by combining the power of LLM with rich and proprietary data sets. These data sets can range from internal documents, databases, to APIs and research papers. This approach uplifts the capabilities of LLMs from providing generic responses to delivering domain specific responses.
This blog post (fourth in the Uncovering GenAI series) picks apart the RAG paradigm, and dives deeper. It explains the basics and then moves to exploring what realistic RAG systems look like.
.NET, and Semantic Kernel. We would use Ollama to run our local LLM.
By the end of this post, you'll have a working example you can run entirely on your own machine.