Posts Tagged → llm

Ask this site how RAG works and you get a full mechanism walkthrough; ask what the surrounding vocabulary means and you land here. This seventh glossary lesson collects the RAG terms that fill vendor docs, product notes, and our own RAG writing practices on AWS, from grounding to lost in the middle. Each entry gets… Continue reading →

This site has taught the mechanisms at length: how AI agents work end to end, and what an MCP server actually is. This eighth glossary lesson does the lighter job: the agent terms themselves, from planning to handoffs to the protocols that let agents reach tools and each other. Each entry is a sentence or… Continue reading →

This domain has its own series on the site: start with Learn LLM Inference and work through to serving engines. This ninth glossary lesson handles the vocabulary itself, the inference terms that appear in API references, model cards, and GPU invoices: sampling dials, prefill and decode, batching, quantization, and the economics at the end of… Continue reading →

No mechanism post on this site teaches fine-tuning end to end yet; the nearest neighbor is how vertical small language models work, where adaptation is the whole business case. For the bigger picture of tuned versus prompted behavior, our LLM concepts deep dive rounds it out. This tenth glossary lesson therefore carries the vocabulary itself:… Continue reading →

Model releases describe a strange second life: a base model finishes pretraining, then goes through a round of tuning that changes its manners completely. That second life is where the alignment terms live; the values side of the story continues in our lesson on responsible AI use. Our reinforcement learning explainer covers the machinery these… Continue reading →

Every model launch arrives holding a fistful of numbers: an MMLU score here, an Arena rank there, a pass rate on a coding set. Our evals explainer shows you how to build an evaluation for your own workload; this lesson gives you the LLM evaluation terms for reading everyone’s. The LLM concepts deep dive shows… Continue reading →

Ship an AI feature and two vocabularies arrive together: the safety language from our responsible AI guide and the attacker language that fills hardening guides like this AgentCore walkthrough. This lesson separates the families and adds a third: the AI safety terms researchers use when they open a trained network and look at the wiring…. Continue reading →

Ask a reasoning model a hard question and it pauses, works, and only then answers. The pause has a vocabulary now, and it shows up in two places you may already know from this site: the ReAct paper, where a model alternates thinking with acting, and Claude’s effort dial, which is a reasoning budget wearing… Continue reading →

Feed a notebook of PDFs, slides, and a recorded lecture to NotebookLM and it answers questions about all of them at once. That product moment is where most readers first meet the multimodal AI terms in this lesson, which stretch across three territories: models that handle images, systems that handle speech, and models that handle… Continue reading →

A model that works in a notebook is a demo; the vocabulary in our machine learning system design primer is what it wears once real traffic arrives, and the AgentOps guide shows the same words repurposed for agents. This final lesson gathers the LLMOps terms and the infrastructure language around them: the chips, the lifecycle,… Continue reading →