Why Data Conditioning Matters

Poor data quality leads to poor RAG performance. Conditioning is essential.

Impact of Poor Data

Dirty Data → Poor Embeddings → Irrelevant Retrieval → Bad Answers

Common Data Quality Issues

1. Encoding problems: '�' characters
2. Formatting artifacts: \r\n\r\n
3. Duplicate content: Same doc indexed multiple times
4. Noise: Headers, footers, page numbers
5. Missing metadata: No dates, authors, sources

Quality Metrics

def assess_data_quality(text):
    return {
        'has_encoding_errors': contains_invalid_chars(text),
        'duplicate_phrases': count_duplicates(text),
        'noise_ratio': calculate_noise(text),
        'metadata_completeness': check_metadata(text)
    }

ROI of Conditioning

Without conditioning:

• Retrieval precision: 60%
• User satisfaction: 3/5

With conditioning:

• Retrieval precision: 85%
• User satisfaction: 4.5/5

Next: Deduplication strategies.