Calling an LLM API is the easy part. Shipping reliable AI features in production demands evaluation pipelines, retrieval layers, observability, cost controls, and guardrails. Here is what real AI engineering looks like in 2026.
Introduction Retrieval‑augmented generation (RAG) has become the de‑facto pattern for building LLM‑powered applications that need up‑to‑date, factual, or domain‑specific knowledge. By coupling a retriever (which fetches relevant documents) with a generator (which synthesizes a response), RAG mitigates hallucination, reduces latency, and lowers inference cost compared with prompting a massive model on raw text alone. While academic prototypes often rely on a single vector store and a simple similarity search, production deployments quickly hit limits: ...
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. ...