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AI knowledge map

AI knowledge map

This page is a navigation guide to all AI-related explainers in the DecAIHub wiki. It is designed for newcomers — whether you are reading your first project card or trying to understand what "AI/Agents" means in a category label. Use it as a table of contents, a concept tree, and a set of recommended reading paths.

In short: this is a map, not an encyclopedia. Every heading below links to a standalone article. Start where your question is, follow the links, come back here when you need orientation.

Bookmark this page if you frequently switch between verification topics (what counts as proof) and technology topics (what a model or agent is). The sections below are ordered so you can move from high-level concepts to specialized cluster pages without losing your place.

Start here

If you are new to DecAIHub and crypto AI verification, read these four articles first:

  1. What is artificial intelligence — the broadest starting point.
  2. What is an AI component — the central concept in every project card.
  3. How to read a project card — what each field means and where to look.
  4. What is DecAI Fit — the 0-6 score that summarizes a project's verifiability.

Concept tree

The articles in this wiki form a hierarchy. Indentation below shows parent-child relationships.

Reading paths

"I am reading my first project card"

  1. What is an AI component — understand the core field.
  2. AI component vs use case — learn why these are separate.
  3. How composite AI categories work — decode the category label.
  4. How to read a project card — full walkthrough of every field.

"I want to understand if the AI is real"

  1. How to tell if a project really has AI — the verification framework.
  2. What counts as evidence of AI — what qualifies as proof.
  3. What does not count as evidence — common false positives.
  4. AI washing, Onchain washing, Token washing — red flags.

"I want to know what a specific AI category means"

  1. Pick the category: AI/Agents | AI/Compute | AI/Inference | AI/Data | AI/Analytics | AI/Training.
  2. Read the matching component article: Agents | Compute | Inference | Data | Analytics | Training.
  3. For agent-specific nuances: What is an AI agent and Assistant, copilot, chatbot, agent.

"I want to understand the AI technology itself"

  1. What is artificial intelligence — the field overview.
  2. What is an AI model — the core building block.
  3. What is machine learning — how models learn from data.
  4. What is a large language model — the most common model type in crypto AI.
  5. What is inference — how a trained model produces outputs.
  6. Benchmarks and quality metrics — how model quality is measured.

Frequently asked questions

Do I need to read every linked article?

No. Use this page to choose a path; each explainer stands alone. Return here when you need a reminder of how topics connect — for example, how evidence tiers relate to AI component wording.

Where should I go if one article uses a term I do not know?

Open the Concept tree above and follow the parent article (for example, from LLM up to artificial intelligence), or jump to How to read a project card for field definitions.

External references

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