Closed & Open Source LLMs

Improving LLM’s Reasoning In Production - The Structured Approach

This guide on achieving better reasoning performance of LLMs intends to complement theguide on prompt engineering by programmatic and systematic to increase flexibility while keeping the amount of operational variability to a minimum.

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.

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.

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 ‍

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.