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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

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

	# install DSPy: pip install dspy
	import dspy

	# Ollam is now compatible with OpenAI APIs
	#
	# To get this to work you must include `model_type='chat'` in the `dspy.OpenAI` call.
	# If you do not include this you will get an error.
	#
	# I have also found that `stop='\n\n'` is required to get the model to stop generating text after the ansewr is complete.
	# At least with mistral.

	1. # create new .py file with code found below
	2. # install ollama
	3. # install model you want “ollama run mistral”
	4. conda create -n autogen python=3.11
	5. conda activate autogen
	6. which python
	7. python -m pip install pyautogen
	7. ollama run mistral
	8. ollama run codellama
	9. # open new terminal

	import os
	import autogen
	import memgpt.autogen.memgpt_agent as memgpt_autogen
	import memgpt.autogen.interface as autogen_interface
	import memgpt.agent as agent
	import memgpt.system as system
	import memgpt.utils as utils
	import memgpt.presets as presets
	import memgpt.constants as constants
	import memgpt.personas.personas as personas

	# %%
	import os
	import openai
	# import autogen
	from autogen import Agent, ConversableAgent, oai, UserProxyAgent, AssistantAgent
	import types
	from dotenv import load_dotenv, find_dotenv
	from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

	_ = load_dotenv(find_dotenv()) # read local .env file

	# %%
	#https://github.com/microsoft/autogen
	import os
	import openai
	import autogen
	import types
	from dotenv import load_dotenv, find_dotenv

	_ = load_dotenv(find_dotenv()) # read local .env file
	openai.api_key = os.environ['OPENAI_API_KEY']