History and Future of LLMs
LLMs are marvels of modern technology. They’re complex in their function, massive in size, and enable groundbreaking advancements. Go over the history and future of LLMs.
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Why the newest LLMs use a MoE (Mixture of Experts) architecture
Mixture of Experts (MoE) architecture is defined by a mix or blend of different “expert” models working together to complete a specific problem.
How Retrieval Augment Generation (RAG) makes LLMs smarter than before
By feeding LLMs the necessary domain knowledge, prompts can be given context and yield better results. RAG can decrease hallucination along with several other advantages.
Quantization and LLMs – Condensing models to manageable sizes
The scale and complexity of LLMs The incredible abilities of LLMs are powered by their vast neural networks which are made up of billions of… Read More »Quantization and LLMs – Condensing models to manageable sizes
Choosing the right technique: Prompt engineering vs fine-tuning
Artificial intelligence and machine learning applications have been revolutionizing many industries for the last decade, but due to generative AI models like ChatGPT, Bard, Midjourney,… Read More »Choosing the right technique: Prompt engineering vs fine-tuning
The emergence of prompt engineers: The next in-demand role in AI
Prompt engineers are emerging as key players in the development and optimization of AI models as artificial intelligence (AI) continues its evolution and becomes an… Read More »The emergence of prompt engineers: The next in-demand role in AI
Can we boost the confidence scores of LLM answers with the help of knowledge graphs?
Irene Politkoff, Founder and Chief Product Evangelist at semantic modeling tools provider TopQuadrant, posted this description of the large language model (LLM) ChatGPT: “ChatGPT doesn’t… Read More »Can we boost the confidence scores of LLM answers with the help of knowledge graphs?