High-performance structured-output models like DAKSH cannot rely solely on raw, unprocessed text. To ensure semantic clarity, structural consistency, and schema alignment, DAKSH's training pipeline incorporates a multi-layered data preprocessing and annotation framework. This rigorous preparation ensures that the model is not only trained on relevant content but also guided by structure and context — essential for downstream generation tasks involving JSON, tables, and policy-bound formats.
The first step in this process is semantic chunking, where documents are segmented into coherent units based on headings, paragraph boundaries, list markers, and logical structure. Each chunk is assigned positional metadata, such as section identifiers, parent document references, and language tags, enabling precise retrieval and context stitching during inference.
Following this, each chunk undergoes typological tagging. Chunks are labeled with content types such as instruction, condition, definition, response, table, or glossary. These tags enhance the retrieval system’s ability to prioritize content that best matches the intent of a query, and they help the model generate appropriately formatted responses.
The pipeline also applies comprehensive noise removal routines. Common artifacts such as HTML tags, footers, navigation menus, watermarks, and boilerplate legal disclaimers are systematically stripped. Additionally, duplicate content blocks — often a result of document versioning or embedded templates — are detected and de-duplicated.
In multilingual deployments, language normalization is critical. DAKSH applies a transliteration-augmented cleaning process to resolve code-mixed expressions, dialectal phrases, and regional variations into canonical forms. This improves embedding alignment and response fluency across languages.
Finally, for structured data, a schema encoding layer is applied. Manually verified examples — including forms, configurations, nested responses, and tabular formats — are tagged with explicit schema identifiers. This instructs the model to recognize not just the meaning but also the required output format, facilitating reliable integration into dashboards, automations, and downstream analytics tools.
A final audit and validation phase ensures that the curated dataset is free of personally identifiable information (PII), structurally ambiguous examples, or biased content — safeguarding the integrity and compliance of the training corpus.