-
Retrieval-Augmented Generation: Limits and Future
The rapid growth of artificial intelligence systems capable of generating text, code, and structured knowledge has dramatically transformed the technology landscape during the 2020s. Large language models have demonstrated an impressive ability to answer questions, summarize documents, and assist with complex analytical tasks. Yet despite their capabilities, these models face a fundamental limitation: they rely…
-
Sparse Attention: When Less Context Is More
In the early years of modern neural language models, the dominant strategy for improving artificial intelligence systems was simple: provide the model with more data, larger context windows, and increasingly complex architectures. Transformers, first introduced in 2017, quickly became the backbone of natural language processing systems because of their ability to evaluate relationships between every…
-
Why Small Language Models Are Returning in 2026
For several years the artificial intelligence industry seemed to move in only one direction: bigger models, larger datasets, and exponentially growing computational requirements. From 2020 to 2024, the dominant belief in AI research was that scaling up neural networks would continue to deliver better reasoning, language understanding, and creative abilities. Massive large language models with…