2.3. Data Validation and Processing Pipelines

💡 First Principle: The quality of an FM's output is bounded by the quality of its input — garbage in, garbage out is more severe for FMs than for traditional software because the model will confidently generate plausible-looking garbage rather than returning an error. Data quality must be validated and enforced before data reaches the model.

This is where many GenAI architectures fail silently. A document with corrupted encoding, a truncated JSON record, or an invoice scan with low OCR confidence produces confident but wrong FM outputs. The pipeline that prepares data for FM consumption is as critical as the FM itself — and Domain 1 tests it extensively.

⚠️ Think of the data pipeline as a customs checkpoint: raw data must be inspected, formatted, and cleared before it's allowed into the FM's context window. Each stage catches failures the next stage can't recover from:

Raw Input → [Extract] → [Validate] → [Normalize] → [Chunk] → [Embed] → FM Context
               ↓              ↓             ↓           ↓         ↓
           Textract       Schema check   JSON format  Size limit  Vector DB
           Transcribe     PII detect     UTF-8 encode  Overlap    Index

Common Misconception: You can pass raw data directly to a foundation model without preprocessing. In practice, FMs require properly structured JSON inputs, token limits respected, conversation history formatted correctly, and data quality validated upstream. The Bedrock API will accept malformed data but the model's output quality degrades silently.