train-model-knowledge-injection

Knowledge injection from PDFs and Markdown tracks source-to-artifact lineage by parsing text into chunks and datasets before model training. This preserves auditability across the entire pipeline from source repositories to the deployable model package.

Choosing between RAG and fine-tuning depends on freshness needs and behavioral control requirements. A hybrid training strategy automatically selects the optimal approach to ensure factual grounding and validated response behavior in the final model.

Evaluation gates enforce correctness, hallucination control, faithfulness to sources, and latency or cost thresholds during model training. The repair loop iteratively remediates only the failing pipeline stages to ensure the model passes objective quality checks.

Yes, secret and PII redaction occurs before isolated dataset splitting during model training. This strict data preparation satisfies license and data checks, ensuring sensitive information is removed from the parsed text and chunks prior to evaluation.

Packaging an evaluated model from a Git repository produces reproducible outputs including a deployment runbook and an evaluation report with pass or fail gates. This ensures the trained model artifact is validated and ready for deployment.

Model training applies to chat QA, code assistance, extraction, classification, and agent behavior tasks. It ensures factual grounding and response behavior are validated through strict evaluation gates before the model is deployed.