Browsing: LLM

Image by Editor   # Introduction  The rise of large language models (LLMs) like GPT-4, Llama, and Claude has changed the world of artificial intelligence. These models…

In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models represent items…

def normalize_ws(s: str) -> str: return re.sub(r”\s+”, ” “, s).strip() RAW_DOCS = [ { “doc_id”: “trulens_core”, “title”: “TruLens core idea”, “text”: “TruLens is used to track…

Have you ever asked an LLM a question, changed the wording a few times, and still felt the answer wasn’t quite right? If you’ve worked with…

For the last few years, the AI world has followed a simple rule: if you want a Large Language Model (LLM) to solve a harder problem,…

Image by Author   # Introduction  Large language models became truly fast when Groq introduced its own custom processing architecture called the Groq Language Processing Unit LPU.…

Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability,…

Enterprises are increasingly shifting from relying solely on large, general-purpose language models to developing specialized large language models (LLMs) fine-tuned on their own proprietary data. Although…

In the post Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI, we introduced the Amazon Nova LLM-as-a-judge capability, which is a specialized…

In this tutorial, we build a robust, multi-layered safety filter designed to defend large language models against adaptive and paraphrased attacks. We combine semantic similarity analysis,…