Tag: LLMs
Embeddings in Large Language Models: How Meaning Is Represented in Vector Space
Explore how embeddings transform language into vector space, enabling AI to understand meaning. Learn about the evolution from Word2Vec to BERT, key applications in RAG and search, and future trends in multimodal AI.
In-Context Learning Explained: How LLMs Learn from Prompts Without Training
In-Context Learning allows LLMs to adapt to new tasks using examples in prompts-no retraining needed. Discover how it works, its benefits, limitations, and real-world applications in AI today.
Positional Encoding in Transformers: Sinusoidal vs Learned for Large Language Models
Sinusoidal and learned positional encodings were the original ways transformers handled word order. Today, they're outdated. RoPE and ALiBi dominate modern LLMs with far better long-context performance. Here's what you need to know.
