Blog Posts

E-Learning Platforms

What Is Khanmigo? - The Chatbot Tutor Based On GPT-4

The integration of ChatGPT and GPT-4 in edtech has revolutionized the way we approach education. These powerful AI technologies have been seamlessly integrated into major edtech platforms, such as Khan Academy, Quizlet, and Duolingo, to enhance the learning experience. With the ability to act as personal tutors, teacher assistants, and feedback systems, ChatGPT and GPT-4 have opened up a world of possibilities for personalized and interactive learning.

Legal Assistants

AI For Lawyers? - Considerations For Using Legal AI In Case Law Research & More

The legal industry is experiencing a significant transformation with the adoption of artificial intelligence (AI) tools. These tools, powered by machine learning algorithms, are revolutionizing the way lawyers work and facilitating case law research internally. While some lawyers may be hesitant about relying on AI due to the importance of accuracy and critical thinking in their work, research shows that up to 22% of a lawyer's tasks can be automated with AI. AI tools can analyze large volumes of legal documents, identify relevant case law, and provide valuable insights, saving time and improving efficiency in case law research.

Customer Relations

What Is Intercom? - How Intercom's AI Transforms Customer Support

Intercom's chatbot called Fin, powered by machine learning and natural language processing, provides personalized and efficient customer support by understanding and responding to user queries, integrating with LLMs, and continuously learning and improving over time.

Closed & Open Source LLMs

Prompt Engineering In Production - The Structured Approach

Large Language Models (LLMs) like GPT-4 have transformed the way we interact with AI, but to achieve reliable and consistent results in real-world applications, we need a more structured approach to prompting to keep the amount of operational variability to a minimum.

Kubernetes & OpenShift

IaaS vs. IaC Explained - A Kubernetes Approach

In today's rapidly evolving technology landscape, businesses are constantly seeking ways to stay ahead of the curve. The world of cloud computing has revolutionized the way businesses operate, offering unprecedented flexibility, scalability, efficiency and security. Two key concepts that have emerged in this landscape are Infrastructure as a Service (IaaS) and Infrastructure as Code (IaC). We will explore how they can work together in a containerized environment, leveraging Kubernetes for better management of your applications.

Closed & Open Source LLMs

LLM Frontiers & Use Case Topology - Risk vs. Potential

In recent years, the rapid advancements in Large Language Models (LLMs) have transformed the landscape of natural language processing and artificial intelligence. As investment in LLMs surges, we witness an astounding growth in their capabilities, ranging from enhanced language understanding and generation to domain-specific expertise.

Closed & Open Source LLMs

LLMs vs. Traditional ML Algorithms - A Pragmatic Comparison

LLMs (like GPT-4) excel in natural language understanding and generation tasks, offering powerful capabilities for processing and generating human language. They are not designed for handling structured data, clustering, image analysis, or ranking structured data tasks, where other machine learning models and algorithms would be more appropriate. But more about that ‍

Closed & Open Source LLMs

Unleashing the Power of GPT-4 - Industry Use Cases Explained

The release of GPT-4, OpenAI's newest natural language processing (NLP) technology, promises to revolutionize industries with its advanced capabilities. Building on previous iterations, GPT-4 offers even more powerful AI features, making it a game-changer in the world of artificial intelligence.

Cloud Computing

What is Model Serving Exactly? - An Example With Amazon Web Services (AWS)

Why Model Serving? Model Serving is an important step in the machine learning lifecycle when creating an AI application. It involves taking the model that's been trained with a dataset and making it accessible for prediction or inference requests.