Schemas
Define analysis tasks in natural language. Apply them at scale.
What Are Schemas?
Schemas are reusable analysis templates in plain English. Define what to extract from documents, AI applies it consistently across documents. Example: Media bias analysis schema:Source Type
Type: Text
Extract: Primary source - government, activist, expert, or anonymous
Extract: Primary source - government, activist, expert, or anonymous
Emotional Intensity
Type: Number (1-10 scale)
Extract: How emotionally charged is the language?
Extract: How emotionally charged is the language?
Framing
Type: Text
Extract: Issue framing - economic, security, moral, or procedural
Extract: Issue framing - economic, security, moral, or procedural
Geographic Scope
Type: Text
Extract: Geographic scope - local, national, or international
Extract: Geographic scope - local, national, or international
Creating Your First Schema
Step 1: Define Your Questions
Start with the analytical questions you want to answer:- What policy positions are mentioned?
- How do different sources frame the same issue?
- What stakeholders are involved?
- What’s the timeline for implementation?
Step 2: Write Clear Instructions
Good instructions:Department
Type: Text
Extract: Department or agency name
Extract: Department or agency name
Amount (Millions)
Type: Number
Extract: Amount in millions of dollars
Extract: Amount in millions of dollars
Change Type
Type: Text
Extract: Change from previous year - increase, decrease, or same
Extract: Change from previous year - increase, decrease, or same
❌ Financial Info
Type: Text
Extract: Extract important financial information
Too vague - AI won’t know what specific information to extract
Extract: Extract important financial information
Too vague - AI won’t know what specific information to extract
Step 3: Choose Data Types
Text (string): Categories, descriptions, names, locationsNumbers: Ratings, amounts, counts, scales
Lists (array): Multiple items, themes, categories
Dates: Timestamps, deadlines, events
Data Type Examples
Text Example
Source Type
Type: Text
Extract: News source - mainstream, alternative, government, or independent
Type: Text
Extract: News source - mainstream, alternative, government, or independent
List Example
Framing Categories
Type: List
Extract: Issue framing - can include multiple: economic, security, moral, procedural
Type: List
Extract: Issue framing - can include multiple: economic, security, moral, procedural
Number Example
Security Stance
Type: Number (1-10 scale)
Extract: Immigration position (1=pro-immigration, 10=security-focused)
Type: Number (1-10 scale)
Extract: Immigration position (1=pro-immigration, 10=security-focused)
Step 4: Test and Refine
Always test on 2-3 sample documents first:- Check output quality matches expectations
- Identify gaps in extracted data
- Refine instructions based on results
How It Works
Pattern: Domain expertise → Natural language instructions → Structured JSON output Benefits:- No coding required
- Reproducible results
- Transparent methodology
- Continuously improvable
Running Analysis
1
Select Content
Choose individual assets or entire bundles to analyse
2
Pick Schema
Select your analysis template from available schemas
3
Configure Settings
Choose AI model and processing options
4
Run Analysis
Process all selected content with your schema
AI Models
Cloud Models: OpenAI GPT, Google Gemini, Anthropic ClaudeLocal Models: Ollama (Llama, Mistral, Code Llama)
Best Practices
Write Specific Instructions
Good - Clear and specific:✓ Department
Type: Text
Extract: Department name (e.g., Education, Defense, Health)
Clear examples help AI understand what you want
Extract: Department name (e.g., Education, Defense, Health)
Clear examples help AI understand what you want
✓ Budget Amount
Type: Number
Extract: Budget amount in millions of dollars
Specific unit makes extraction consistent
Extract: Budget amount in millions of dollars
Specific unit makes extraction consistent
❌ Financial Info
Type: Text
Extract: Extract important financial information
AI won’t know which financial details matter to you
Extract: Extract important financial information
AI won’t know which financial details matter to you
Start Simple, Then Expand
- Extract basic entities (people, organisations, locations)
- Add analysis layers (sentiment, categorization)
- Include complex reasoning (relationships, advanced analysis)
Test Before Scaling
Always test on 2-3 sample documents before processing large batches.
Sharing Schemas
Schema Library: Upload successful schemas and browse others’ workTransparency: Others can see, critique, and replicate your methodology
Community: Builds cumulative knowledge in the field
Next Steps
1
Create Schema
Start with simple entity extraction and test on sample documents
2
Upload Content
Use Assets & Bundles to organise your documents
3
Run Analysis
Apply your schema and choose appropriate AI models
4
Explore Results
Visualise findings with Analysis Dashboards
5
Scale Up
Create complex schemas and explore Chat & MCP