AI in practice, part 4: From RAG to tools and agents

Mon, 26 Oct 2026 18:30:00 -0400

RAG lets a model read what it didn’t know. Tools let it do things. An agent is what you get when the model decides, step by step, which tools to use until the job is done.

Three levels of “the model knows something”
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Only its training. The model answers from patterns learned before its cutoff. It can’t see your files and doesn’t know today’s date unless you tell it.
Retrieval (RAG). Your application searches your documents and pastes the relevant passages into the prompt. The model reads, then answers. I built one of these here. The application decides what to retrieve.
Tools. The model itself asks for something to be done: “search the docs for X”, “run this query”, “create the ticket”. The model decides when and how; your application carries it out.

Function calling, step by step
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With tool use (often called function calling), you describe each tool to the model: a name, a description and a JSON schema for its arguments.

Building a Practical Retrieval-Augmented Personal Assistant (RAG)

Tue, 11 Nov 2025 12:00:00 +0000

The elements of information discussed here are all present in my github repository.

with Ollama, LangChain, and Chroma

1. Why I Built This
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Large Language Models are powerful, but they hallucinate and forget your private knowledge. We set out to build a small, local Retrieval-Augmented Generation (RAG) assistant that:

Rag on Samarthya

AI in practice, part 4: From RAG to tools and agents

Three levels of “the model knows something” #

Function calling, step by step #

Building a Practical Retrieval-Augmented Personal Assistant (RAG)

1. Why I Built This #

Three levels of “the model knows something”
#

Function calling, step by step
#

1. Why I Built This
#