Format
Explainers
Plain-language guides to concepts and techniques. Every explainer published by GenAI Brief, selected across topics.
41 stories · page 4 of 4
ToolsExplainer
MCP gateway explained: what it does and when agent teams need one
An MCP gateway puts one governed endpoint between agents and the MCP servers they call. How tool filtering, upstream auth, audit and token control work, and where the risks sit.
ResearchExplainer
What reasoning models actually do: a training recipe and a token budget
Reasoning models are ordinary LLMs post-trained with RL on checkable problems, then given a thinking budget at inference. The lineage, the training loop, the costs, and where the gains actually land.
ToolsExplainer
Semantic caching for LLMs: how it works and where it goes wrong
Semantic caching replays a stored LLM answer when a new prompt means the same thing. How thresholds, vector stores and cache keys work, how it differs from prompt caching, and how to measure savings.
IndustryExplainer
The real cost of an AI product, line by line: tokens are rarely the biggest line
A sourced ledger of what a production AI feature costs in September 2026: inference, retrieval, agents, evaluation, gateways, review and people, with a worked monthly model at three volumes.
ToolsExplainer
What is an AI gateway? How it works, when you need one and how to choose
An AI gateway sits between applications and model providers to handle keys, budgets, failover, caching, guardrails and logs. How the request path works, and when a team actually needs one.
