AI-Insights-Unveiling-the-Future-with-AI-Technologies-and-LLMs
Discover the captivating world of cutting-edge AI technologies, mind-boggling Language Models (LLMs), and much more! This repository is a treasure trove of insightful blogposts that dive deep into the realm of artificial intelligence, guiding you through the latest advancements, industry trends, and practical applications.
Are you ready to embark on an exhilarating journey into the realm of AI? Look no further! AI Insights is your go-to resource for comprehensive articles on AI, LLMs, and related topics. From beginner-friendly introductions to advanced techniques, we cover it all, unraveling the mysteries and demystifying the complexities of this rapidly evolving field.
Below are the various topics; check them out and implement them wherever possible.
1. Build and Deploy Your Personal ChatGPT Bot in Python with ChatGPT API on MacOS :
Are you ready to harness the limitless potential of ChatGPT? Imagine having an AI-powered terminal/Shell assistant that provides instant support, automates complex tasks and saves valuable time. Need a tricky awk or Linux/shell command? Your assistant has got you covered!
With the gpt-3.5-turbo-16k model, your Personal ChatGPT bot not only trains on the data you provide but also leverages the vast amount of knowledge from the extensive data it has access to. The possibilities are endless!
In just a few lines of Python code, you can have an assistant who answers any question, provides solutions, and gives personalized advice from your terminal or shell. It’s like having your very own AI sidekick!
Boost your productivity, streamline your workflows, and elevate your AI experience. So follow along and keep reading till the end.
GitHub Report for the above which contains the source code and everything is here
2 Model Parameters in OpenAI API
This post breaks down the ChatCompletion.create() Function and find the sweet spot for the parameter values together.
Temperature: Controlling andomness and diversity of the Responses
Max Tokens: Limiting the Response Length
Top P (Nucleus Sampling): Controlling Response Quality
n: Generating Multiple Responses
Stop: Customizing Stop Conditions
Frequency Penalty: Controlling Repetitive Responses
Presence Penalty: Controlling Avoidance of Certain Topics3 Best Practices for Securing Your OpenAI API Key
This post explains the Best Practices for Securing Your OpenAI API Keys. Keep your API key secure as environment variables and not share it with anyone. Using HTTPS protocol for all requests and responses and limiting the scope of your API key to only the necessary permissions.
4 Personal ChatGPT Bot with Streamlit: Build Your AI Assistant Web app
Do you want your own AI-powered web-app that can understand your questions and provide helpful answers? Look no further! With the power of Python and Streamlit combined with OpenAI APIs, building a personalized ChatGPT bot is easier than you might think.
5 VeLO: The Intelligent Neural Network Optimizer Revolutionizing Deep Learning and Automating Optimization
VeLO, is a system designed to act as a fully tuned optimizer. It uses a neural network to compute the target network’s updates — A neural network to train and auto-tune another neural network.
6 ChatBase: Build an AI Chatbot from Your Data and Transform Your Interaction Experience with data
ChatBase let's you craft AI chatbots from your data like standard files(PDF, .docx, .txt, .csv, etc.), text files, crawl websites, and much more. Then add it as a widget to your website, blogs using embeds, links or chat with it through its API or build apps on top of it for your Company’s team by training the bot on company’s data securely.
7 How to Master the Art of ChatGPT Prompt Engineering
Prompt engineering is crafting well-defined instructions or stimuli, called prompts, to elicit specific and desired responses from AI language models like ChatGPT. It involves formulating prompts that provide clear context, specify desired outcomes, and guide the AI model toward generating accurate and relevant outputs.