Normalization transforms data into a consistent scale, representation, or format. In generative AI architectures, it is a preprocessing step that makes data more suitable for LLM outcomes, retrieval, and model adaptation. section 10.4.1 section 8.4.1
Apply normalization as part of a broader data-preparation workflow:
- Clean and standardize data before it informs an LLM, alongside deduplication and error rectification. section 10.4.1
- Plan normalization in retriever pipelines with noise removal and dimensionality reduction, while retaining information relevant to the use case. section 8.4.1
- Prepare fine-tuning data through text normalization, including tokenization, and convert it into the model’s required input format. section 9.2.1
Fine-tuning dataset preparation
A task-specific dataset can be cleaned, text-normalized through tokenization, labeled, and converted into the input format required by the selected LLM before fine-tuning. section 9.2.1
Normalization does not replace data governance or validation. Production architectures still require data-quality automation, access controls, encryption, compliance assessment, and testing to protect sensitive data and verify pipeline performance. section 10.4.1 section 8.4.1
