IndyDevDan disler

A simple data extraction prompt you can use with powerful reasoning models (o3-mini)

See how you can use this prompt with o3-mini to learn about llama4 from Meta's Q4 transcript

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

Prompt Chaining with QwQ, Qwen, o1-mini, Ollama, and LLM

Here we explore prompt chaining with local reasoning models in combination with base models. With shockingly powerful local models like QwQ and Qwen, we can build some powerful prompt chains that let us tap into their capabilities in a immediately useful, local, private, AND free way.

Explore the idea of building prompt chains where the first is a powerful reasoning model that generates a response, and then use a base model to extract the response.

Play with the prompts and models to see what works best for your use cases. Use the o1 series to see how qwq compares.

Setup

Bun (to run bun run chain.ts ...)

Four Level framework for prompt engineering

Watch the breakdown here in a Q4 2024 prompt engineering update video

LLM library

Ollama

Level 1: Ad hoc prompt

Quick, natural language prompts for rapid prototyping
Perfect for exploring model capabilities and behaviors

Minimal Prompt Chainables

Sequential prompt chaining in one method with context and output back-referencing.

Files

main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chain
chain.py - contains zero library minimal prompt chain class
chain_test.py - tests for chain.py, you can ignore this
requirements.py - python requirements

Setup

This is not working complete code.

This is strictly a v0.2, scrapy, proof of concept version of a personal AI Assistant working end to end in just ~726 LOC.

This is the second iteration showcasing the two-way prompt aka multi-step human in the loop. The initial, v0, assistant version is here.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

This is not working complete code.

This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

Stay focused, keep building.

Setup

Create a new directory with these three files (requirements.txt, main.py, README.md)
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py
Update main() to run the example prompt chains

	# /// script
	# dependencies = [
	# "requests<3",
	# "rich",
	# ]
	# ///

	# https://docs.astral.sh/uv/guides/scripts/#declaring-script-dependencies

	import requests

	import subprocess
	import os

	def download_yt_script(url: str) -> str:
	"""
	Download and extract script from YouTube video

	Credit:
	Original Code: https://github.com/davidgasquez/dotfiles/blob/bb9df4a369dbaef95ca0c35642de491c7dd41269/shell/zshrc#L75
	Simonw Blog: https://simonwillison.net/2024/Dec/19/