Large Language Models have revolutionized artificial intelligence, enabling machines to understand and generate human-like text at scale. But not all LLMs are created equal. Understanding the different types, architectures, and approaches to LLM development is essential for developers and AI enthusiasts looking to leverage these powerful tools effectively. This comprehensive guide walks you through the landscape of Large Language Models, from foundational concepts to practical implementation strategies. Table of Contents What Are Large Language Models? Core LLM Architectures LLM Categories and Classifications Major LLM Families and Examples Comparing LLM Types: Strengths and Weaknesses Choosing the Right LLM for Your Use Case Practical Implementation Tips Top 10 Learning Resources What Are Large Language Models? A Large Language Model (LLM) is a deep learning algorithm trained on vast amounts of text data to understand, summarize, translate, predict, and generate human-like content.[3] These models represent one of the most significant breakthroughs in artificial intelligence, enabling applications from chatbots to code generation. ...

As an expert LLM infrastructure engineer, I’ve deployed countless inference systems where time-to-first-token (TTFT) and GPU efficiency make or break production performance. Enter LMCache—a game-changing KV cache layer that delivers 3-10x delay reductions by enabling “prefill-once, reuse-everywhere” semantics across serving engines like vLLM.[1][2] This zero-to-hero tutorial takes you from conceptual understanding to production deployment, covering architecture, integration, pitfalls, and real-world wins. Whether you’re building multi-turn chatbots or RAG pipelines, LMCache will transform your LLM serving stack. ...

Table of Contents Introduction What is Cache-Augmented Generation? Why CAG Matters CAG vs RAG: A Detailed Comparison How Caching Works in LLMs Conceptual Implementation Practical Implementation Example Common Pitfalls and Solutions Cache Invalidation Strategies Production Best Practices Top 10 Learning Resources Introduction Large Language Models (LLMs) have revolutionized how we build intelligent applications, but they come with a critical challenge: latency and cost. Every query requires processing tokens, which translates to computational overhead and API expenses. Cache-Augmented Generation (CAG) represents a paradigm shift in how we augment LLMs with knowledge, offering a faster, more efficient alternative to traditional retrieval-based approaches. ...

BM25 (Best Matching 25) is a probabilistic ranking function that powers modern search engines by scoring document relevance based on query terms, term frequency saturation, inverse document frequency, and document length normalization. As an information retrieval engineer, you’ll use BM25 for precise lexical matching in applications like Elasticsearch, Azure Search, and custom retrievers—outperforming TF-IDF while complementing semantic embeddings in hybrid systems.[1][3][4] This zero-to-hero tutorial takes you from basics to production-ready implementation, pitfalls, tuning, and strategic decisions on when to choose BM25 over vectors or hybrids. ...

Introduction vLLM has quickly become one of the most popular inference engines for serving large language models efficiently, thanks to its paged attention and strong OpenAI-compatible API. But as soon as you move beyond a single GPU or a single model server, you run into familiar infrastructure questions: How do I distribute traffic across multiple vLLM servers? How do I handle failures and keep latency predictable? How do I roll out new model versions without breaking clients? This is where the vLLM Router comes in. ...