Prompt
Here's an example of how I'm currently capturing conversations with LLMs in Obsidian. What do you think about the structure. How do you think it could be improved? And based on this approach, how do you recommend I configure the automation I'd like whereby just the prompts are copied into the Prompts folder?
Output
Your current structure in Obsidian, where you capture conversations with LLMs, looks well-organized and functional. However, there are a few ways you might consider improving it to make it even more effective and streamlined:
1. File Naming Convention
- Improvement: Use a consistent naming convention that includes the date and a brief description of the session's purpose. This will make it easier to find specific conversations later on.
- Example:
2024-09-25_GPT_Workbench_Ideas.md
2. Separating Prompts and Outputs
- Improvement: Consider separating the prompts and outputs into distinct sections within each note. This can help with clarity and make it easier to extract prompts for reuse.
- Example:
# Prompt (Your prompt here) # Output (The generated content here)
3. Tagging System
- Improvement: Implement a tagging system to categorize your notes by topic, type of prompt, or project. This will help you quickly find related prompts or outputs.
- Example Tags:
#idea
,#content
,#projectX
4. Linking Related Notes
- Improvement: Use Obsidian's internal linking feature to connect related notes, such as linking specific outputs back to the prompt templates or related projects.
- Example:
2024-09-25_GPT_Workbench_Ideas
Automation to Copy Prompts to the Prompts Folder
Option 1: Using Obsidian Plugins
- Templater Plugin:
- Create a template that automatically saves a new prompt into the
Prompts
folder whenever you start a new conversation. - Configure the template to pull in the prompt text and save it separately as a new file in
Prompts
. - Dataview Plugin:
- Use the Dataview plugin to create a query that finds all notes with a section titled "Prompt" and copies the content into a new file in the
Prompts
folder. - QuickAdd Plugin:
- Set up a QuickAdd macro that extracts the "Prompt" section of your notes and automatically creates a new note in the
Prompts
folder.
Option 2: External Scripting
- Python Script: Write a Python script that reads through your Obsidian vault, extracts the "Prompt" sections from relevant notes, and copies them into new markdown files in the
Prompts
folder. You can run this script periodically or trigger it manually.
Basic Example of a Python Script:
import os
import re
# Define your Obsidian vault path
vault_path = '/path/to/your/obsidian/vault'
prompts_folder = os.path.join(vault_path, 'Prompts')
# Create the prompts folder if it doesn't exist
os.makedirs(prompts_folder, exist_ok=True)
# Iterate through your markdown files in the vault
for root, dirs, files in os.walk(vault_path):
for file in files:
if file.endswith('.md'):
file_path = os.path.join(root, file)
with open(file_path, 'r') as f:
content = f.read()
# Regex to extract the prompt section
match = re.search(r'# Prompt\n(.+?)\n# Output', content, re.DOTALL)
if match:
prompt_text = match.group(1).strip()
# Save the prompt to a new markdown file
prompt_file_name = f"{file.replace('.md', '')}_prompt.md"
prompt_file_path = os.path.join(prompts_folder, prompt_file_name)
with open(prompt_file_path, 'w') as prompt_file:
prompt_file.write(f'# {file.replace(".md", "")} Prompt\n\n{prompt_text}')
print("Prompts have been successfully copied to the Prompts folder.")
Summary
- Improve your structure by implementing consistent file naming, separating prompts and outputs, using tags, and linking related notes.
- Automate the process of copying prompts using Obsidian plugins or a custom Python script, depending on your preference.
This setup should help you efficiently organize and manage your GPT interactions in Obsidian.## Extracted Prompts Prompts/Autocapture/How can I improve this structure for storing GPT outputs in Obsidian.md#Prompt 1
Extracted Prompts
Prompts/How can I improve this structure for storing GPT outputs in Obsidian.md#Prompt 1
The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.