GenAI Use Cases
===============

Generative AI (GenAI) workflows typically span multiple stages—from **data preparation** to **model fine-tuning**, **prompt engineering**, **evaluation**, and **deployment**. Kubeflow Pipelines provides a flexible and scalable orchestration engine to support these end-to-end workflows in a reproducible, modular way.

Data Preparation
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Effective GenAI starts with high-quality, well-structured data. Use Kubeflow Pipelines to:

- Ingest and preprocess unstructured data such as PDFs, HTML, images, or audio.
- Convert raw documents into structured formats and chunk them for tokenization.
- Clean, normalize, and deduplicate datasets for training and evaluation.
- Generate embeddings using models like SentenceTransformers or CLIP.
- Create and store metadata-rich artifacts for traceability and downstream reuse.

Fine-tuning & Training
----------------------
Once data is prepared, Kubeflow Pipelines can orchestrate training jobs at scale:

- Automate tokenization and model fine-tuning (e.g., LoRA, full fine-tuning).
- Parallelize hyperparameter sweeps (e.g., learning rate, batch size) using conditional and parallel components.
- Leverage GPUs, TPUs, or managed training backends across environments.
- Use pipeline components to separate data prep, training, and checkpoint saving.

Prompt Engineering Experiments
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Experiment with prompt templates using parameterized pipelines:

- Evaluate prompt effectiveness at scale using batch scoring jobs.
- Log and compare model outputs with evaluation metrics and annotations.
- Enable iterative prompt design with easy-to-swap text templates.

Evaluation & Monitoring
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Build pipelines to evaluate and monitor model outputs:

- Compare generations against reference outputs using BLEU, ROUGE, or custom metrics.
- Integrate human-in-the-loop review and scoring.
- Run periodic evaluation pipelines to detect degradation or drift in output quality.

Inference & Deployment
----------------------
Turn generative models into production services with reproducible deployment steps:

- Package and deploy models as containerized services using KServe or custom backends.
- Use CI/CD pipelines to roll out new versions with A/B testing or canary releases.
- Scale endpoints dynamically based on request volume and latency metrics.

Multimodal Generative Workflows
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Design rich pipelines that support multiple input/output modalities:

- Combine text, image, and audio generation into a unified DAG.
- Orchestrate complex workflows involving model chaining and data routing.
- Use custom components to process modality-specific inputs and outputs.


See Also
--------
- :doc:`dsl`
- :doc:`components`
- :doc:`compiler`
