content quality assessment

---
title: Content Quality Assessment
category: concepts
created: 2026-04-21
updated: 2026-04-21
tags: [content-quality, triage-system, automated-filtering, knowledge-management, relevance-scoring, source-evaluation]
sources: [raw/conversations/2026-04-21-cursor-empty-window-a2ebec5b.md]
confidence: high
---

# Content Quality Assessment

Systematic approach for evaluating content relevance and quality before ingestion into knowledge management systems. Essential for preventing knowledge base pollution while maximizing valuable content capture.

## Three-Tier Scoring System

### High Quality (Immediate Ingestion)
- Introduces concepts, techniques, or patterns not yet in knowledge base
- Contradicts or significantly nuances existing documented knowledge  
- Directly relates to active projects or core domains
- Originates from high-quality sources (papers, official docs, recognized authors)
- Contains actionable code patterns or architecture decisions

### Medium Quality (Review Queue)
- Potentially relevant but uncertain value proposition
- Adjacent to core domains but not directly applicable
- Interesting but shallow - might need deeper sources on same topic
- Requires human judgment for final decision

### Low Quality (Archive/Discard)  
- Not related to defined domains of interest
- Too superficial (brief social media posts without substance)
- Duplicate of already-ingested content
- Promotional content disguised as technical content
- Outdated information already superseded

## Implementation Considerations

### Relevance Filtering
For personal content (screenshots, mixed feeds):
- Quick domain check: "Is this tech/AI/engineering related?"
- Avoids deep analysis of irrelevant personal content
- Preserves compute budget for valuable content

### Adaptive Thresholds
Quality scoring improves as knowledge base grows:
- Basic transformer article scores "low" when 15 pages already exist
- Novel technique on same topic scores "high"  
- System becomes naturally more selective over time

### Human Feedback Loop
Medium-scored content in review queue provides training data:
- Track human accept/reject decisions
- Refine scoring criteria in system configuration
- Continuous improvement of automated judgment

## Technical Implementation

LLM compares new content against existing wiki index and applies structured criteria:

Is this content:

• Novel relative to existing knowledge?
• From a credible source?
• Actionable/specific rather than generic?
• Substantial enough to warrant wiki space?

Result feeds into ingestion pipeline routing decision.

## See also

- [multi-source-ingestion](/concepts/multi-source-ingestion)
- [automated-research](/concepts/automated-research)
- [knowledge-management-systems](/concepts/knowledge-management-systems)