> ## Documentation Index
> Fetch the complete documentation index at: https://jobo.world/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Example Flows
> Pre-built pipeline examples for common job data transformation use cases.
## Extract Requirements & Responsibilities
Parse job descriptions to extract structured requirements and responsibilities using AI, then merge back with the original data.
### Flow
```mermaid theme={null}
graph LR
A["🟢 Input
Jobs Feed"] -->|raw job JSON| B["🟣 AI
Extract Req & Resp"]
A -->|original data| C["🟠JSON Merge
Deep / Patch Wins"]
B -->|extracted fields| C
C -->|enriched job| D["🔴 Output
Database"]
style A fill:#f0fdf4,stroke:#15803d,color:#15803d
style B fill:#f5f3ff,stroke:#7c3aed,color:#7c3aed
style C fill:#fffbeb,stroke:#b45309,color:#b45309
style D fill:#fef2f2,stroke:#dc2626,color:#dc2626
```
### Configuration
**AI Node:**
* **Model:** `google/gemini-2.0-flash-001`
* **System Prompt:** *"You are a job data extraction assistant. Extract requirements (must-have and preferred) and key responsibilities from the job description. Always respond with valid JSON."*
* **User Prompt:** *"Extract requirements and responsibilities from this job posting:\n\n\{\{input}}"*
* **Output Schema:**
```json theme={null}
{
"type": "object",
"properties": {
"requirements": {
"type": "object",
"properties": {
"must_have": { "type": "array", "items": { "type": "string" } },
"preferred": { "type": "array", "items": { "type": "string" } }
}
},
"responsibilities": { "type": "array", "items": { "type": "string" } }
}
}
```
**JSON Merge Node:**
* **Strategy:** Deep
* **Conflict Resolution:** Patch Wins
### Result
The original job object is enriched with structured `requirements` and `responsibilities` fields extracted by AI. All original fields (title, company, location, etc.) are preserved through the merge.
***
## Generate SEO Metadata
Generate SEO-optimized titles, meta descriptions, slugs, and keywords for job postings. Uses a Liquid node to prepare a focused context first, reducing AI token usage.
### Flow
```mermaid theme={null}
graph LR
A["🟢 Input
Jobs Feed"] -->|raw job JSON| B["🔵 Liquid
Prepare Context"]
B -->|focused context| C["🟣 AI
Generate SEO"]
A -->|original data| D["🟠JSON Merge
Deep / Patch Wins"]
C -->|SEO fields| D
D -->|enriched job| E["🔴 Output
Database"]
style A fill:#f0fdf4,stroke:#15803d,color:#15803d
style B fill:#f0f9ff,stroke:#0369a1,color:#0369a1
style C fill:#f5f3ff,stroke:#7c3aed,color:#7c3aed
style D fill:#fffbeb,stroke:#b45309,color:#b45309
style E fill:#fef2f2,stroke:#dc2626,color:#dc2626
```
### Configuration
**Liquid Node** — prepares a concise context for the AI:
```liquid theme={null}
{
"title": "{{ data.title }}",
"company": "{{ data.company.name }}",
"location": "{{ data.location.city }}, {{ data.location.country }}",
"is_remote": {{ data.is_remote }},
"description_preview": "{{ data.description | strip_html | truncate_words: 100 }}"
}
```
**AI Node:**
* **Model:** `google/gemini-2.0-flash-001`
* **System Prompt:** *"Generate SEO metadata for a job posting. Respond with valid JSON only."*
* **User Prompt:** *"Generate SEO-optimized metadata for this job:\n\n\{\{input}}"*
* **Output Schema:**
```json theme={null}
{
"type": "object",
"properties": {
"seo_title": { "type": "string", "description": "Max 60 characters" },
"meta_description": {
"type": "string",
"description": "Max 155 characters"
},
"slug": { "type": "string", "description": "URL-safe slug" },
"keywords": {
"type": "array",
"items": { "type": "string" },
"description": "5-10 keywords"
}
}
}
```
**JSON Merge Node:**
* **Strategy:** Deep
* **Conflict Resolution:** Patch Wins
### Result
The original job object gains SEO fields (`seo_title`, `meta_description`, `slug`, `keywords`) while preserving all original data. Ready for search engine indexing.
***
## Schema Mapping
Use a Liquid node to remap job fields to match your database schema. No AI needed — this is a pure Liquid transformation that runs in sub-millisecond time.
### Flow
```mermaid theme={null}
graph LR
A["🟢 Input
Jobs Feed"] -->|Jobo schema| B["🔵 Liquid
Remap Fields"]
B -->|your schema| C["🔴 Output
Your Database"]
style A fill:#f0fdf4,stroke:#15803d,color:#15803d
style B fill:#f0f9ff,stroke:#0369a1,color:#0369a1
style C fill:#fef2f2,stroke:#dc2626,color:#dc2626
```
### Configuration
**Liquid Node** — remaps Jobo's schema to your internal database schema:
```liquid theme={null}
{
"external_id": "{{ data.id }}",
"job_title": "{{ data.title }}",
"employer_name": "{{ data.company.name }}",
"employer_logo": "{{ data.company.logo }}",
"employer_url": "{{ data.company.url }}",
"city": "{{ data.location.city }}",
"state": "{{ data.location.state }}",
"country": "{{ data.location.country }}",
"remote_allowed": {{ data.is_remote }},
"description_html": "{{ data.description | json_escape }}",
"apply_link": "{{ data.apply_url }}",
"date_posted": "{{ data.published_at | date: '%Y-%m-%d' }}",
"slug": "{{ data.title | slugify }}-{{ data.company.name | slugify }}-{{ data.id | slice: 0, 8 }}",
"tags": [{% for tag in data.tags %}"{{ tag }}"{% unless forloop.last %}, {% endunless %}{% endfor %}]
}
```
### Result
Each job is remapped from Jobo's standard schema to your custom database schema. Because this uses only Liquid (no AI), it runs in **under 1 millisecond** per job and produces **100% deterministic** results.
Schema mapping is the simplest pipeline pattern and a great starting point. If
you only need to rename fields and format dates, you don't need AI at all.
***
## When to Use Each Pattern
| Use Case | Pattern | Nodes Used | Latency |
| -------------------------------------- | --------------------------- | ------------------------ | --------------- |
| Field renaming / reformatting | Schema Mapping | Liquid only | \< 1ms |
| AI enrichment (skills, classification) | Branch & Merge | AI + JSON Merge | 1–5s |
| SEO generation with context prep | Prepare → Transform → Merge | Liquid + AI + JSON Merge | 1–5s |
| Multi-field extraction | Parallel AI | Multiple AI + JSON Merge | 1–5s (parallel) |