Key Concepts

Understanding these core concepts will help you get the most from qualcode.ai and produce publication-ready results.

Projects

A project is a container for your coding work. Think of it as a folder that organizes everything related to a single study or research question.

Each project contains:

• Data files: Your uploaded CSV or Excel files with survey responses
• Coding runs: The results of coding a specific column with a specific guide
• History: A record of all coding activities and exports

Coding Guides

A coding guide defines how responses should be classified. It's the set of categories and rules the AI raters use to code your data.

Each coding guide includes:

• Categories: The codes or labels you want to assign (e.g., "Product Quality", "Customer Service", "Pricing")
• Descriptions: Clear explanations of what belongs in each category
• Mode: Single-label (one category per response) or multi-label (multiple categories allowed)
• Training data: Optional examples that teach the AI your coding style

Coding guides are reusable across projects. Create a guide once, then apply it to multiple data files or surveys that use the same categories.

Dual-Rater Methodology

qualcode.ai's defining feature is its dual-rater approach. Instead of using a single AI model, two independent AI systems code each response:

Why Two Raters?

This approach mirrors traditional inter-rater reliability (IRR) studies in qualitative research:

• Agreement metrics: Calculate Cohen's Kappa, Krippendorff's Alpha, and percent agreement
• Genuine independence: The models use different architectures and training data
• Disagreement detection: Cases where raters disagree are flagged for human review
• Methodological credibility: Results meet academic standards for reliability reporting

Training Data

Training data consists of example responses with their correct category assignments. It teaches the AI how you want responses classified.

Zero-Shot Mode (No Training Data)

When you provide no training examples, qualcode.ai operates in zero-shot mode:

• AI raters use only your category names and descriptions
• Works well for straightforward, well-defined categories
• Fastest way to get started
• Good for initial exploration of your data

Few-Shot Mode (With Training Data)

Adding training examples switches to few-shot mode:

• AI raters learn from your specific examples
• Improves accuracy for nuanced or domain-specific categories
• Helps handle edge cases and ambiguous responses
• Training data is versioned, so you can track improvements

Credits

Credits are the currency for AI processing in qualcode.ai. Each coding run consumes credits based on a simple formula:

credits = responses x model_factor x context_factor

Free Credits

Every new account can receive up to 500 free credits: 50 credits immediately on signup, and 450 more after verifying your email. That's enough to code approximately 330 responses at Standard quality. No credit card required.

Reconciliation

Reconciliation is the process of resolving disagreements between the two AI raters. When Rater A and Rater B assign different categories to a response, you decide the correct code.

The Reconciliation Process

1. Review disagreements: See responses where raters disagree, along with both suggested codes
2. Make decisions: Choose the correct category (or assign a different one)
3. Build training data: Your decisions can become training examples for future runs

Active Learning Loop

Reconciliation creates an active learning loop:

• Your corrections teach the system your coding preferences
• Future runs benefit from your previous decisions
• Accuracy improves over time as training data grows

Next Steps

Now that you understand the core concepts:

• Quick Start - Get your first coding run in 5 minutes
• Coding Guide Best Practices - Design effective categories
• Agreement Calculation - Interpret reliability metrics
• FAQ - Answers to common questions