Machine Learning

Large Language Models (LLMs) are great at generating fluent text, but on their own they do not know about your internal company documents, current configurations, or live systems. Building a useful AI assistant requires connecting the model to external knowledge and giving it the ability to search, verify facts, and use tools.

This blog post introduces the main concepts behind modern LLM applications. We will start with why basic Retrieval-Augmented Generation (RAG) often fails, and then look at how to improve search queries, compress context, and build more capable systems using Agentic RAG, Graph RAG, and LLM-Wikis.

Naive RAG and Why It Fails

Retrieval-Augmented Generation (RAG) is the standard way to let an LLM answer questions using your own …

An activation function is used to introduce non-linearity in an artificial neural network. It allows us to model a class label or score that varies non-linearly with independent variables. Non-linearity means that the output cannot be replicated from a linear combination of inputs; this allows the model to learn complex mappings from the available data, and thus the network becomes a universal approximator. On the other hand, a model which uses a linear function (i.e. no activation function) is unable to make sense of complicated data, such as, speech, videos, etc. and is effective only up to a single layer.

To allow backpropagation through the network, the selected activation function should be differentiable. This property is required to compute …