This blog shows how to program using the DSPy framework. As means to that end, it walks us through a proof of concept conducted for an easy to understand use case involving SEO of digital text-based courses. It covers Inline and class-based signatures and shows the results of using a class-based signature with both the Predict and the ChainOfThought module (with multiple completion steps). The prompts that are generated under the hood are also shown. There is discussion on how the optimizers like LabeledFewShot, BootstrapFewShot and BootstrapFewShotRandomSearch work behind the scenes. The blog also shows how to write a customized metric function, create training and test data, and finetune using two of the optimizers. It shares some reflections on the results obtained.

Let's discuss vector database vs. graph database: Vector databases handle high-dimensional data for tasks like similarity searches, while graph databases model complex relationships for applications such as social network analysis.

In the rapidly evolving AI landscape, Ollama has emerged as a powerful open-source tool for running large language models (LLMs) locally. This guide explores Ollama’s features and how it enables the creation of Retrieval-Augmented Generation (RAG) chatbots using Streamlit. With simple installation, wide model support, and efficient resource management, Ollama makes AI capabilities accessible on personal computers. It supports custom model creation and offers a RESTful API for integration. By combining Ollama with LangChain, developers can build advanced chatbots capable of processing documents and providing dynamic responses. This blog walks through setting up the environment, managing models, and creating a RAG chatbot, highlighting the practical applications of Ollama in AI development.

The text explores how artificial intelligence (AI) is transforming software design by enhancing development processes, improving user experiences, and enabling data-driven decision-making. It highlights the integration of AI in areas such as automated coding, debugging, and user interaction, while also emphasizing the importance of scalability, data management, and collaboration among interdisciplinary teams. Overall, it illustrates AI's significant role in creating more efficient, adaptable, and user-centric design practices.

Explore large vision models (LVMs) such as CLIP, ViT, and DINOv2 and discover how they revolutionize computer vision by enhancing image and video processing capabilities across various industries.

Get started with the innovative RAPTOR technique.

This blog explains the basics of generative AI, from its origins in traditional language models to modern techniques like self-supervised learning and fine-tuning, highlighting its impact, applications, and future potential. Understand the magic behind generative AI!

Large language models (LLMs) have revolutionized Natural Language Processing (NLP), powering applications like chatbots and content creation. In 2024, the top LLMs include GPT-4, Google Gemini, and many more. We'll explore the top language models influencing NLP in this blog.

Generative AI has revolutionized creativity and innovation across industries, from art to text generation. This quick guide explores how these models work, their benefits, challenges, and their role in data augmentation, content creation, and personalization. It also highlights popular generative AI models like GPT-3, DALL·E, and Midjourney, emphasizing the importance of learning GenAI skills for developers.