8 articles tagged #LLM
Many Cursor AI users struggle with inaccurate code suggestions because they're providing the wrong context. Learn the difference between Notepads and Context Files, when to use each, and how proper context management dramatically improves AI-generated code quality.
Your Retrieval-Augmented Generation (RAG) application performs well during testing but suddenly experiences latency spikes in production. The problem isn't always the LLM. Learn how to identify whether your bottleneck is the embedding service, vector database, document retrieval, network, reranking,
Bigger context windows don't automatically produce better AI responses. In many cases, stuffing an LLM with excessive conversation history, documents, or retrieved passages reduces answer quality, increases latency, and introduces distractions. Learn why context window bloat occurs and how to keep
Large Language Models don't hallucinate randomly. Certain categories of questions consistently produce higher rates of fabricated facts, nonexistent citations, invented APIs, and incorrect reasoning. Understanding these hallucination hotspots helps developers design safer AI applications
LLM agents often perform well during the first few interactions but begin forgetting important details as conversations grow longer. This phenomenon—often called multi-turn memory collapse—is usually caused by context limitations, summarization strategies, retrieval failures, or prompt design
Your transformer model performs well after fine-tuning on a new dataset, but suddenly becomes much worse at the task it originally mastered. This phenomenon, known as catastrophic forgetting, is one of the biggest challenges in continual learning. Learn why it happens and practical strategies
Semantic caching can dramatically reduce LLM costs and response times, but many teams discover that seemingly identical questions still bypass the cache and trigger expensive model calls. Learn why semantic cache misses occur, how embedding similarity works, and how to build production-ready caching
Large Language Models don't distribute attention evenly across a prompt. Researchers have discovered that certain early tokens can act as "attention sinks," attracting disproportionate attention and influencing outputs in unexpected ways. Learn how attention sink tokens work, why they affect model