Sachin Dharmapurikar dharmapurikar

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

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

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 ...)

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.

	ollama list \| Select-Object -Skip 1 \| ForEach-Object {
	$model = ($_ -split '\s+')[0]
	if ($model -and $model -ne "NAME") {
	Write-Host "Updating model: $model"
	ollama pull $model
	}
	}

	import asyncio
	import time

	async def run_powershell_command(command):
	process = await asyncio.create_subprocess_exec(
	"powershell", "-Command", command,
	stdout=asyncio.subprocess.PIPE,
	stderr=asyncio.subprocess.PIPE
	)

	#!/bin/bash
	export STARSHIP_DOCKER_CONTAINER="dharmapurikar/poetry:3.11" # Replace this with the base container you are using

	if [ -n "$STARSHIP_DOCKER_CONTAINER" ]; then
	docker run "$STARSHIP_DOCKER_CONTAINER" python -c 'import sys; print(sys.version.split()[0])'
	fi

	"$schema" = 'https://starship.rs/config-schema.json'

	format = """
	[](color_orange)\
	$os\
	$username\
	[](bg:color_yellow fg:color_orange)\
	$directory\
	[](fg:color_yellow bg:color_aqua)\
	$git_branch\

	{
	"keys": [
	"start",
	"end"
	],
	"values": [
	[
	"1363842000",
	"1364446800"
	],