---
title: "Taxonomy Design"
description: Analyze all content files to identify patterns, series, and topics. Design a comprehensive frontmatter schema for classification.
---

# Taxonomy Design

## The Pattern: Let AI Analyze Your Data

Before you can classify content, you need to understand what's in it. The traditional approach: manually read through files, take notes, identify patterns, design categories. With 209 files, that's days of work.

Instead, let the AI agent read everything and propose a taxonomy.

## The Prompt

> look through all the emails in @content/uncategorized. We want to build a taxonomy of the data, where we identify high level series, propose categories and topics, and what other metadata we want to consistently extract from the data. Propose a content schema that we could apply to all of the emails

The `@` symbol brings the entire directory into context. The agent reads all 209 files and identifies patterns.

## The Agent's Analysis

Based on reading every file, the agent identifies:

### Series Patterns

| Series                    | Title Pattern     | Count | Description                               |
| ------------------------- | ----------------- | ----- | ----------------------------------------- |
| FOD (Findings of the Day) | `FOD#N:`          | ~99   | Weekly digest of AI news and papers       |
| Topic Deep Dives          | `Topic N:`        | ~4    | Technical explainers on specific concepts |
| Agentic Series            | `🦸🏻#N:`           | ~18   | Agent and reasoning system deep dives     |
| SF Insights               | `🌁#N:`           | ~20   | San Francisco-based AI insights           |
| Concept Cards             | `Concepts:`       | ~5    | Short explainers of AI concepts           |
| Podcast/Interviews        | `🎙️`              | ~20   | Conversations with AI practitioners       |
| Unicorn Profiles          | Company structure | ~10   | Deep-dive company profiles                |

### Content Types

| Type           | Description                           | Example              |
| -------------- | ------------------------------------- | -------------------- |
| `digest`       | Weekly roundup with multiple sections | FOD emails           |
| `explainer`    | Technical deep-dive on a concept      | Topic emails         |
| `article`      | General article or essay              | Standalone pieces    |
| `interview`    | Q&A or conversation format            | Podcast transcripts  |
| `profile`      | Company or person profile             | Unicorn series       |
| `curated`      | Lists, recommendations, roundups      | Book recommendations |
| `announcement` | Newsletter meta-content               | Welcome messages     |
| `sponsored`    | Partner/sponsored content             | Webinar promotions   |
| `forwarded`    | Forwarded email (not original)        | `Fwd:` prefix        |
| `survey`       | Reader survey or feedback request     | Input requests       |

### Topic Categories

The agent identifies primary topic areas:

| Value            | Description                               | Common Keywords               |
| ---------------- | ----------------------------------------- | ----------------------------- |
| `llm`            | Language models, architectures, training  | transformer, GPT, attention   |
| `agents`         | AI agents, agentic workflows, multi-agent | agent, workflow, tool use     |
| `infrastructure` | ML ops, training, optimization            | FSDP, inference, GPU, scaling |
| `research`       | Academic papers, benchmarks, methods      | paper, benchmark, SOTA        |
| `industry`       | Company news, funding, launches           | unicorn, funding, valuation   |
| `business`       | Enterprise AI, productivity               | enterprise, ROI, adoption     |
| `ethics`         | AI safety, policy, regulation             | safety, alignment, bias       |
| `hardware`       | Chips, robotics, physical AI              | GPU, TPU, robot, chip         |
| `multimodal`     | Vision, audio, video AI                   | image, video, vision          |

## Refining with Human Judgment

> we should also add to this the list of people mentioned. write out the report, focusing on describing the content schema which will be used to populate the frontmatter of the emails, so make sure it's in a form thats good for that

You steer the agent to add people tracking. The final schema includes:

```yaml
people_mentioned:
  - name: "string" # Full name
    role: "string" # Their role/title (optional)
    affiliation: "string" # Company/institution (optional)
```

The report is saved to `reports/2025-12-01-content-taxonomy-schema.md`. It includes:

1. **Series identification patterns** — Regex and emoji detection
2. **Complete frontmatter schema** — All fields with types and allowed values
3. **People tracking** — name/role/affiliation structure
4. **Four detailed examples** — FOD digest, Topic explainer, Unicorn profile, Podcast interview
5. **Notable people reference** — Frequently mentioned individuals for entity recognition
6. **Exclusion criteria** — What to filter out (forwarded emails, surveys)
7. **Implementation notes** — Regex hints for automated extraction

## The Complete Schema

### Required Fields

| Field          | Type           | Values                                                                                                                  |
| -------------- | -------------- | ----------------------------------------------------------------------------------------------------------------------- |
| `title`        | string         | Clean article title                                                                                                     |
| `date`         | `YYYY-MM-DD`   | Publication date                                                                                                        |
| `series`       | string or null | `fod`, `topic`, `agentic`, `insights`, `concepts`, `podcast`, `unicorn`, `guestpost`, or `null`                         |
| `content_type` | string         | `article`, `digest`, `explainer`, `interview`, `profile`, `curated`, `announcement`, `sponsored`, `forwarded`, `survey` |

### Categorization

| Field           | Type             | Values                                                                                                    |
| --------------- | ---------------- | --------------------------------------------------------------------------------------------------------- |
| `primary_topic` | string           | `llm`, `agents`, `infrastructure`, `research`, `industry`, `business`, `ethics`, `hardware`, `multimodal` |
| `tags`          | array of strings | Specific technologies, techniques, or concepts                                                            |
| `episode`       | integer          | Episode number (for numbered series)                                                                      |

### People & Organizations

| Field                 | Type                                   |
| --------------------- | -------------------------------------- |
| `people_mentioned`    | Array of `{name, role?, affiliation?}` |
| `companies_mentioned` | Array of strings                       |
| `models_mentioned`    | Array of strings                       |

### Content Characteristics

| Field                 | Type    | Values                                      |
| --------------------- | ------- | ------------------------------------------- |
| `audience`            | string  | `technical`, `general`, `business`, `mixed` |
| `depth`               | string  | `overview`, `intermediate`, `deep-dive`     |
| `has_research_papers` | boolean |                                             |
| `has_code_examples`   | boolean |                                             |
| `is_premium`          | boolean |                                             |

## Why Design the Schema First

This is a critical pattern: **define your output before you build the pipeline.** The schema serves as:

1. **A contract** between the extraction phase and classification phase
2. **A target** for the classification agent to aim at
3. **A validation reference** for catching errors
4. **A discovery tool** — the act of designing the schema reveals what's in your data

Without a schema, the classification agent would produce inconsistent output. With it, every file follows the same structure, making downstream processing (search, filtering, cross-linking) reliable.

## The Meta-Pattern

The taxonomy report itself is an example of a compounding pattern:

1. Agent reads all content → identifies patterns
2. Human reviews → adds people tracking
3. Refined schema → written to `reports/`
4. Schema report → becomes context for classification agent
5. Classification agent → applies schema to every file

Each step builds on the last. The schema report isn't just documentation — it's **machine-readable context** that future agents consume.

---

**Next: [Parallel Classification](/content-repurpose/03-classification/07-parallel-agents/)** — Create the classifier and process 50 files at once.