THE AIGNOSTIC

Category: GPT

  • Understanding Privacy in OpenAI’s API: A Comprehensive Guide

    Understanding Privacy in OpenAI’s API: A Comprehensive Guide

    In today’s AI-driven world, data privacy has become a paramount concern for developers and organizations utilizing AI APIs. When integrating OpenAI’s powerful API capabilities into your applications, understanding the platform’s privacy framework isn’t just good practice—it’s essential for maintaining data security and ensuring compliance with various regulatory requirements.

    The Privacy Foundation

    At its core, OpenAI’s approach to API privacy centers on a fundamental principle: your data remains yours. This commitment manifests through several key privacy measures that protect user interests while enabling innovative AI applications.

    Data Handling and Retention

    One of the most significant privacy advantages of OpenAI’s API is its approach to data usage. Contrary to what some might assume, OpenAI does not use API inputs or outputs to train its models. This means your queries and the responses you receive remain private and won’t be incorporated into future model updates.

    The platform maintains API usage logs for approximately 30 days—a practice claimed purely for system monitoring and troubleshooting. These logs serve operational purposes only and are not utilized for model enhancement or training.

    Ownership and Control

    OpenAI’s terms of use explicitly confirm that users retain ownership of both their input data and the generated outputs. This clear stance on data ownership is particularly crucial for businesses handling proprietary information or developing competitive applications.

    Security Infrastructure

    Privacy goes hand in hand with security, and OpenAI implements robust measures to protect data:

    • Strong encryption protocols safeguard data during transmission and storage
    • Comprehensive security measures protect against unauthorized access
    • Regular security audits and updates maintain system integrity

    Regulatory Compliance

    In today’s global marketplace, regulatory compliance is non-negotiable. OpenAI acknowledges this by aligning with major data privacy regulations:

    • GDPR compliance for European users
    • CCPA alignment for California residents
    • Support for user rights regarding data access and deletion

    Best Practices for API Privacy

    To maximize privacy when using OpenAI’s API, consider implementing these practical strategies:

    1. Data Minimization
      • Share only necessary information
      • Strip personally identifiable information (PII) from inputs
      • Implement pre-processing filters for sensitive data
    2. Output Management
      • Review API responses before deployment
      • Implement automated scanning for sensitive information
      • Maintain audit logs of API interactions
    3. Enhanced Privacy Options
      • Consider private deployment options for sensitive applications
      • Explore Azure OpenAI Service for additional security layers
      • Implement role-based access controls in your applications

    Considerations for Regulated Industries

    Organizations in regulated sectors face unique challenges. Healthcare providers, financial institutions, and government agencies should:

    • Conduct thorough privacy impact assessments
    • Consult with legal experts on compliance requirements
    • Consider private deployment options
    • Implement additional security layers as needed

    Looking Forward

    As AI technology evolves, privacy considerations will continue to shape API development and usage. OpenAI’s commitment to privacy, combined with user vigilance and best practices, creates a framework for responsible AI implementation.

    The key to successful API integration lies in understanding these privacy measures and implementing them effectively within your specific context. Whether you’re developing a simple chatbot or a complex enterprise solution, making privacy a priority from the start will help ensure sustainable and compliant AI implementation.

    Remember: While this guide provides an overview of OpenAI’s API privacy features, always refer to the official documentation and policies for the most current information, and consult legal experts when handling sensitive data or operating in regulated industries.

  • Automatic AI Author (AAA) for WordPress

    Automatic AI Author (AAA) for WordPress

    Create and post content without human intervention

    Say you had a blog on any topic and wanted AI (OpenAi, xAi) to automatically write or translate existing content for you and post it directly to your WordPress website.

    1. Add user to WordPress with Application Password
      After adding a new User (or use an existing one) set an application password in WordPress (Users -> Edit User) 
    # RSS_AI_Wordpress

import requests
import json
import base64 
from _AI_Writer import get_news_response
response = get_news_response("What are the main headlines today?")

# WordPress API endpoint
url = "https://YOURWEBSITE.com/wp-json/wp/v2/posts"

# Authentication credentials
user = "BOT"
password = "YOUR_APPLICATION_PASSWORT_MATE"
credentials = user + ':' + password
token = base64.b64encode(credentials.encode())
header = {
    'Authorization': 'Basic ' + token.decode('utf-8'),
    'Content-Type': 'application/json; charset=utf-8',
    'Accept': 'application/json, */*',
    'User-Agent': 'Python/RequestsClient'
}

# Post content to WordPress
post = {
    'title': 'AI BOT - Daily News',
    'content': response,
    'status': 'publish',
}

# Send POST request with verify=False to debug SSL issues
response = requests.post(url, headers=header, json=post, verify=True)

# Check if the request was successful
if response.status_code == 201:  # 201 is the success code for creation
    print("Post created successfully!")
    #print(response.json())
else:
    print(f"Error: {response.status_code}")
    print(response.text)

    This code posts automatically to your WordPress blog. The actual content (stored in “response”) we retrieve from a module called _AI_Writer.

    2. Writing Content with Your AI Writer Bot

    Our AI writer module fetches an RSS Feed (Google News in our case; bur could be any website or feed) and writes a short blog post in his own words on today’s news. This gets posted directly to our blog (see code above).

    # _AI_Writer.py

import os
from openai import OpenAI
import feedparser

XAI_API_KEY = "YOUR_XAI_KEY_HERE"
client = OpenAI(
    api_key=XAI_API_KEY,
    base_url="https://api.x.ai/v1",
)

def chat_with_gpt(prompt):
    response = client.chat.completions.create(
        model = "grok-beta",
        messages=[{"role": "user", "content": prompt}],
        #temperature = 0.8,
    )
    return response.choices[0].message.content.strip()

def get_rss_feed(url):
    """Fetch and parse RSS feed from given URL"""
    feed = feedparser.parse(url)
    return feed

def get_feed_entries(feed, limit=10):
    """Extract entries from feed, with optional limit"""
    entries = []
    for entry in feed.entries[:limit]:
        entries.append({
            'title': entry.get('title', ''),
            'link': entry.get('link', ''),
            'published': entry.get('published', ''),
            'summary': entry.get('summary', '')
        })
    return entries

def get_news_response(user_input):
    """Get AI response based on RSS feed news and user input"""
    rss_url = "https://news.google.com/news/rss"
    feed = get_rss_feed(rss_url)
    entries = get_feed_entries(feed)
    
    prompt = f"""Here are the latest news entries. {user_input}

{[entry['title'] + ': ' + entry['summary'] for entry in entries]}"""
    
    return chat_with_gpt(prompt)

# Modified main block for testing
if __name__ == "__main__":
    # Test the module
    response = get_news_response("Please provide a brief summary")
    print("Test response:", response)

    Like all AI workflows this offers a plethora of use cases

    You could have it fill a website with articles without ever touching said website. Or maybe translate content of one website and repost content on another.

    Or maybe – if you are evil – scale this x 1000 and fill hundreds of websites with your propaganda. Unfortunately this is all too easy.

  • Adding Company Data to LLMs with Retrieval Augmented Generation (RAG)

    Adding Company Data to LLMs with Retrieval Augmented Generation (RAG)

    Customization Options for LLMs

    Before we look into Retrieval Augmented Generation a short overview of customisation options of LLMs.

    • Prompt Engineering: Customizes input for better responses.
    • RAG: Integrates external data for immediate context.
    • Fine-Tuning: Alters the model for specific tasks or languages.
    • Pretraining: Involves training from scratch with custom data, very costly and time-intensive.

    What is retrieval-augmented generation?

    Retrieval Augmented Generation (RAG) Framework

    Instead of relying solely on its pre-trained knowledge, the model uses this fresh data to generate responses that are more accurate, up-to-date, and tailored to the context, effectively turning static AI into a dynamic information wizard.

    Challenges Solved by Retrieval Augmented Generation (RAG)

    Problem 1: LLMs Lack Access to Your / New Data

    • LLMs are trained on vast public datasets but can’t access new or private data post-training, leading to outdated or incorrect responses.

    Problem 2: Need for Custom Data in AI Applications

    • Effective AI applications, like customer support bots or internal Q&A systems, require domain-specific knowledge, which static LLMs lack without additional training.

    RAG integrates specific data into the LLM’s prompts, allowing the model to use real-time or custom data for more accurate responses without retraining.

    Use Cases for RAG:

    • Question and Answer Chatbots: Enhances chatbot accuracy by using company documents.
    • Search Augmentation: Improves search results with LLM-generated answers.
    • Knowledge Engine: Enables quick answers from internal data like HR documents.

    Benefits of RAG:

    • Up-to-Date Responses: Provides current information by integrating external data sources.
    • Reduces Hallucinations: Minimizes incorrect or fabricated answers with verified external context.
    • Domain-Specific Answers: Tailors responses to fit organizational data needs.
    • Cost-Effective: Does not require model retraining, saving time and resources.

    RAG vs. Fine-Tuning

    When to Use RAG vs. Fine-Tuning the Model

    Start with RAG if you want a quick, effective solution that leverages real-time data without altering the model’s core behavior. Opt for fine-tuning when you need to modify the model’s behavior or teach it a new domain specific “language.” Remember, these methods can complement each other. Consider fine-tuning for deeper understanding and output precision, while using RAG for up-to-date, contextually relevant responses.

    • Use RAG when you need quick, relevant responses without model changes.
    • Fine-Tune when you want to alter the model’s behavior or language understanding. Both can be used together for optimal results.

  • What is the difference between custom GPTs and Fine-Tuning a GPT?

    What is the difference between custom GPTs and Fine-Tuning a GPT?

    In November 2023 OpenAi rolled out custom GPTs.

    We’re rolling out custom versions of ChatGPT that you can create for a specific purpose—called GPTs. Anyone can easily build their own GPT—no coding is required.

    So the question arises.

    What is the difference between custom GPTs and Fine-Tuning?

    • Custom GPTS are all based on the same AI model that remains unaltered. You « just » give the GPT instructions and documents, they can be modified at anytime to update the GPT to your convenience. Basically you are uploading some PDFs to improve answers.




      Imagine giving a new employee some print outs in order to better answer some basic client phone calls. These manuals might help answer some specific question but will not train your new employee.

    • Fine-tuning is giving new knowledge to an AI by retraining it. You « feed it » new data that will now be part of the AI, therefore changing the core of that specific AI. The whole model needs to be retrained. You are looking at costs of thousands.


      It’s like sending a person someone to some course or university. It will deeply alter that persons knowledge and understanding.

    So in the second case the AI is modified in its core, while in the first case it is all about providing instructions to guide the existing AI. Fine-tuning is more complex and expensive, you need new quality data in the right format.

    More here: https://community.openai.com/t/custom-gpts-vs-fine-tuning-whats-the-difference/477738/2

  • Python ChatGPT Chatbot Tutorial

    Python ChatGPT Chatbot Tutorial

    How to set up your own Chatbot interface with Python in 3 minutes?

    1. Get an OpenAI account and get your API key

      https://platform.openai.com
    2. Install Python and the OpenAI Python library
    pip install openai

    3. Run Python in Visual Studio Code (or wherever)

    import openai
openai.api_key = "sk-YOUR_KEY_COMES_HERE_MATE"

def chat_with_gpt(prompt):
    response = openai.chat.completions.create(
        model = "o1-mini",
        messages=[
            {"role": "assistant", "content": "You are a helpful assistant." },
            {"role": "user", "content": prompt}
            ],
        #temperature = 0.7,
    )
    return response.choices[0].message.content.strip()

if __name__ == "__main__":
    while True:
        user_input = input("Tom: ")
        if user_input.lower() in ["quit", "exit", "bye"]:
            break
        
        response = chat_with_gpt(user_input)
        print("AIgnostic: ", response)

    You can specify any model you would like to test.