Llm on

RAG in Go: A Vulnerability Research Tool

Mon, 20 Apr 2026 00:00:00 +0000

Introduction

In the previous post, you saw how you can use tools to add information to an LLM query. In this post, we’ll see another method of adding information to an LLM called RAG, or Retrieval-Augmented Generation.

The idea of RAG is that you want the LLM to have access to information that wasn’t available to it when it was initially trained. You do it by storing documents in your own database along with their embedding. I won’t go into the technical details of embedding, but think of it as a way to convert a piece of text into a vector. The magic is that if two pieces of text have similar meaning, an embedding model can create vectors that mathematically show they are similar.

Using Tools: A Meeting Scheduler

Mon, 16 Mar 2026 00:00:00 +0000

Introduction

LLMs are great, but they are trained on public data sets. In some cases, you need the LLM to use data that’s not publicly available or that’s frequently changing. There are several ways to make such data available to LLMs:

Tool/function calls
Retrieval-augmented generation (aka RAG)
MCP

In coding agents, you can also add skills.

In this post we’ll focus on function calling.

How Does It Work?

When interacting with an LLM, you can provide a description of available tools if the model supports tool calling. If the LLM reasons that the best answer is to use one of the tools, it will return a reply that contains a tool call with the parameters to use. Then you make the function call and return the answer back to the LLM.

Query Database Using Plain English

Wed, 11 Feb 2026 00:00:00 +0000

Introduction

In this post you’ll see how you can create a system that allows users to query a relational database using plain English. This allows users not familiar with SQL or business intelligence systems to get insights from data.

Setting Up

If you want to follow along, you’ll need to clone the code from the GitHub repo. This will download the code, and the database file containing the data (bikes.ddb)

Note: The data is from the Austin Bike Share dataset.