> ## Documentation Index
> Fetch the complete documentation index at: https://docs.codatta.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Fundamentals
### TL;DR
This page builds shared language for the rest of the docs. It explains how **AI** uses **samples**, **labels**, **validations**, how **Codatta** models them as **atomic contributions**, how they bundle into a **data asset** (the minimal commercial unit), and why we use **blockchain** to make **assetification** & **royalties programmable**.
## Data basics for Artificial Intelligence (AI)
**Sample** (math notion: **X**): A raw observation the model will learn from (image, audio clip, text span, time
series window, multi-sensor frame, etc.).
**Label** (math notion: **Y** or **y**): A structured interpretation of a sample (or group of samples): class, bounding
box, segmentation mask, span, rating, relation, events over time, etc.
**Validation**: A quality judgment or evidence check on a sample or label. This can be consensus
voting, rubric scoring, re-labeling, or automated checks plus human adjudication.
**Why quality matters to models**
* **Signal-to-noise** – mislabeled or low-information data reduces effective batch
size and slows convergence.
* **Bias & leakage** – inconsistent schema, shortcut features, or label leakage
harm generalization and fairness.
* **Heterogeneous tasks** – multi-task/chain-of-thought models depend on **clear,
consistent instructions** and **traceable provenance** to debug and improve.
**Take-away**: Better samples + clearer labels + verifiable validations =
**more useful gradient steps** and **fewer surprises in production**.
## Codatta’s data model
*Figure 1. Samples, labels, and validations aggregate into a **Data Asset**; assets
are then selected into **Datasets**.*
**Why it matters**: The **Data Asset** is the **unit of ownership & royalty** and
the **unit of licensing** most buyers actually consume.
**A. Atomic Contribution (AC)**\
One unit of work produced by a human or agent:
* `sample` – the observation
* `label` – the interpretation
* `validation` – the quality/evidence decision
Every AC is assigned a **Contribution Fingerprint (CF)**:
* A tamper-evident identifier (hash + metadata + parent links) that proves
*who did what, when, to which payload*.
* CFs make contributions **discoverable, deduplicable, and auditable**.
**B. Data Asset (DA)**\
A **composite, minimal commercial unit** created by aggregating ACs that belong
together (e.g., one image + its accepted labels + validations). Ownership and
licensing are enforced at the **asset** level because that’s what AI teams
actually use.
**C. Dataset (View / Collection)**\
A curated selection of **Data Assets** for a particular model, vertical, or
evaluation—defined by a saved query or manifest. Datasets inherit all
**ownership, licensing, and lineage** from the assets they include.
## Typical real-world scenarios
The figures below are **conceptual** and focus on relationships and flow; field
names and formats may evolve as the protocol is finalized.
### Scenarion A: One sample, multiple label sets
*Figure 2. One sample (X0) gets labeled by **task01** and **task02**. Bundling*
`X0 + {Y0, Y1}` *forms Asset-A (Vertical AI “a”), while bundling* `X0 + {Y2}`
*forms Asset-B (Vertical AI “b”).*
**Why it matters**: The same raw sample can power **different products** simply by
bundling **different labels**—each with its own royalties and license terms.
### Scenarion B: Cross-sample composite
*Figure 3. Two samples (X0, X1) are combined into a **composite asset** for a new
task (task03). A downstream label (Y3) annotates the composite, not a single
sample.*
**Why it matters**: Many tasks (dialog pairs, multi-turn contexts, video/action
segments) require **relationships across samples**. Codatta supports composite
assets and keeps **derivation links** for proper attribution and payouts.
### Scenarion C: Label-on-label (meta-labeling)
*Figure 4. A downstream label (Y4) targets an **upstream label** (Y0), which itself
annotated X0. Royalties propagate to the **meta-labelers** and **origin labelers**
(and to the original sample as policy dictates).*
**Why it matters**: You can annotate **interpretations**, not just raw data:
rubrics, explanations, confidence judgments, or evaluator notes—all with lineage
and revenue inheritance.
## Assetification & blockchain technology
**Why assetify?**\
Traditional labeling produces files that are hard to track, share, or value.
**Assetification** turns work into **on-chain objects** with provenance and
programmable rights:
* **Provenance** via **Contribution Fingerprints** (hash + metadata + parents)
→ *who did what, when, to which payload*.
* **Ownership** via **fractional tokens** on Data Assets (not just on files),
so multiple contributors/validators can participate in revenue.
* **Licensing & metering** with policy-gated access (public vs. restricted), and
usage receipts (read/train/infer) that drive **royalty routing**.
* **Derivation** that carries **inheritance rules** (child assets point to
parents; royalties propagate per policy).
* **Privacy-by-design**: hybrid storage + secure compute (e.g., TEEs) so models
can **use** data without exposing raw content.
**What blockchain adds**
* **Trust minimization**: public, append-only record of contributions, ownership,
and usage events.
* **Composability**: assets can be queried, bundled, and re-licensed across apps
while preserving **lineage and payouts**.
* **Incentives**: contributors and validators earn when assets are used—aligning
quality with long-term value.