Plain-English definitions of AI and engineering terms — no fluff, no paywalls.
DevSecOps integrates security into every stage of the software development lifecycle. Learn what shift-left security means, how it works, and why startups should adopt it from day one.
A double materiality assessment evaluates both financial and environmental impacts of sustainability topics. What it is, how it works, and why CSRD requires it.
What are ESRS standards? A plain-English guide: which companies comply, what data to collect, and how AI is changing EU sustainability disclosure workflows.
The EU Taxonomy is a classification system defining environmentally sustainable economic activities. Learn its 6 objectives, how it works, and what it means for startups.
The GHG Protocol is the world's most widely used greenhouse gas accounting standard. Learn Scopes 1, 2, and 3 — and what they mean for your startup's ESG strategy.
GRI Standards are the world's most widely adopted framework for sustainability reporting. Learn what GRI covers, how it works, and why startups need to understand it.
SBTi helps companies set emission reduction targets aligned with climate science. Learn what science-based targets are, how validation works, and why SBTi matters for startups.
Scope 3 emissions are the largest and hardest part of GHG reporting. Learn the 15 categories, where data comes from, and how to build an audit-ready reportin...
A SOC 1 report assesses internal controls over financial reporting. Learn what SSAE 18 requires, the difference between Type I and Type II, and why startups need SOC 1.
SOC 2 Trust Service Criteria cover security, availability, processing integrity, confidentiality, and privacy. Learn what each means and how to achieve SOC 2 compliance.
AI guardrails are rules, filters, and controls that keep LLM outputs safe and on-topic. Learn how they work and why every production AI app needs them.
Embeddings convert text, images, and data into vectors that capture meaning. Learn how AI embeddings work, why they matter, and when to use them.
A knowledge graph stores entities and their relationships as connected nodes. Learn how knowledge graphs power AI reasoning, search, and RAG in production systems.
What is a vector database? It stores high-dimensional embeddings for semantic search. How it works, when to use it, and which ones to choose in 2026.
Agentic AI is AI that plans and executes multi-step tasks autonomously. Learn how it works, how it differs from chatbots, and where it's used in 2026.
AI alignment ensures AI systems pursue goals that match human intentions. Learn the core concepts, key techniques, and why it matters for building safe AI.
AI evaluation measures LLM application quality, safety, and reliability. Learn the key metrics, frameworks, and methods teams use in production.
AI observability means tracking inputs, outputs, latency, and costs of LLM apps in production. Learn why it matters and how to implement it.
AI safety covers the practices, tools, and frameworks that prevent AI systems from causing harm. Learn the key risks and how engineering teams mitigate them.
An AI agent perceives its environment, reasons over inputs, and acts to reach goals without human input. Learn how they work and where they're used today.
An AI MVP is a minimum viable product built around an AI capability. Here's what separates it from a prototype, what it must include, and what to ship first.
Chain-of-thought prompting makes LLMs reason step-by-step before answering. Learn how it works, when to use it, and how it improves accuracy on complex tasks.
CSRD is the EU's mandatory sustainability reporting law affecting 50,000+ companies. Learn who's in scope, what you must report, and how AI cuts prep time.
A double materiality assessment is the cornerstone of CSRD compliance. How to run one, avoid mistakes, and turn a 6-month process into 6 weeks with software.
What is fine-tuning? Training an LLM on your data to change its behavior. When fine-tuning beats prompting — and when RAG is the smarter, cheaper path.
Function calling lets LLMs invoke structured tools and APIs instead of just generating text. Learn how it works, why it matters, and how to use it in 2026.
Grounding anchors AI outputs to verified facts, documents, or real-time data. Learn how it works, why it matters, and how to implement it in production.
Inference optimization makes AI models faster and cheaper to run. Learn the key techniques — quantization, caching, batching — and when each applies to LLM apps.
LLM orchestration connects language models, tools, and memory into chains and pipelines. Learn how it works and when your AI app needs it.
The MCP protocol (Model Context Protocol) is Anthropic's open standard connecting AI agents to external tools and data sources. What it is and why it matters.
Multimodal AI processes text, images, audio, and video in one model. Learn how it works, which models lead, and when to use it in production.
What is prompt injection? The top LLM security vulnerability explained: how it works, real attack examples, and how to prevent it in production AI apps.
What is RAG? It combines a retrieval system with an LLM to answer questions from your own data. How it works, when to use it, and how to build it.
Semantic search finds results by meaning, not exact words. Learn how vector embeddings power modern AI search — and when to use it over keyword search.
Tokenization converts text into numerical tokens before an LLM processes it. Learn how it works, why it matters for cost, and common tokenizer gotchas.
Tool use lets AI models call APIs, run code, and interact with systems beyond their training data. Learn how it works and why it enables true AI agents.