Ai on Daydream in Boston

Advanced Prompting guide

Sun, 22 Mar 2026 00:00:00 +0000

To be honest, the word “Prompt Engineering” sounded like a bit black magic or even alchemy to me; It’s more of an art than a science.

But as all IT engineers are required to improve our productivity using AIs, I decided to learn it and go a little deeper on the subject.

This article is my learning notes as well as a practical guide for advanced prompting. In later sections, I have added insights that I gained through my prompt usage and Q&As with (or “interrogating”) AIs.

RAG workflow PoC - Python, ChromaDB, Ollama

Sun, 15 Mar 2026 00:00:00 +0000

In the previous post, I learned how LLM works from LLM itself (Copilot, to be more specific). In this post, I will learn about RAG, of course, from Copilot.

Methodology:

Ask questions starting from “What is RAG?” to Copilot, and read answers
Write down my understanding, asking Copilot if I’m correct
Revise my writing until my understanding is solid

Let’s start today’s learning.

What is RAG?

RAG (Retrieval-Augmented Generation) is a workflow for retrieving relevant external information and injecting it into user questions for LLM to consume. RAG supplements these LLM’s weak points:

How GenAI works - Transformer internals

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

I didn’t really like GenAI because it hallucinates, consumes lots of energy, has raised memory and SSD prices, etc. But as an IT engineer, I can’t ignore it. In this post, I’ll try to learn how GenAI (LLM) works by asking a lot of questions to AI.

I mainly use Copilot because it is most lenient with hourly/daily usage limit. Below are mainly outputs from Copilot (but I modified/summarized them). Sorry if I didn’t remove all hallucinations. As it turned out, LLM as a technology is pretty interesting. Let’s see if I can learn something complex as LLM from AI.