> ## Documentation Index
> Fetch the complete documentation index at: https://docs.seekr.com/llms.txt
> Use this file to discover all available pages before exploring further.
# AI-ready data
> Generate structured training datasets from raw content for model fine-tuning.
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AI-ready data generates and transforms datasets, converting raw content into training-ready formats. These jobs produce structured outputs optimized for model fine-tuning and alignment.
## How it works
The AI-ready data pipeline transforms uploaded files into structured training datasets:
**File selection** – Choose source files from storage to use as training material
**Dataset type selection** – Specify the type of dataset to generate based on fine-tuning method
**Generation** – The system processes files and creates structured question-and-answer pairs or training examples
**Output** – Generated datasets are saved in formats compatible with fine-tuning workflows
## Integration with fine-tuning
AI-ready datasets feed directly into fine-tuning workflows:
* **Instruction fine-tuning** – Standard instruction datasets train models on task-specific examples
* **Context-grounded fine-tuning** – Context-grounded datasets train models to use retrieval effectively
* **Dataset quality** – Higher quality source content and generation produces better fine-tuned models
The data engine automates the transition from raw files to training-ready datasets, reducing manual dataset preparation effort.
## Datasets for fine-tuning
AI-ready data supports multiple dataset formats aligned to fine-tuning methods:
### Instruction fine-tuning
Standard instruction datasets consist of traditional question-and-answer pairs aligned to task-specific instructions. Each example demonstrates how the model should respond to particular queries or prompts.
**Structure:**
* **Input** – The question, prompt, or instruction
* **Output** – The expected response or completion
**Use cases:**
* Teaching domain-specific knowledge
* Customizing response style and tone
* Training task-specific behaviors
These datasets are used with instruction fine-tuning to embed knowledge directly into model parameters.
### Context-grounded fine-tuning
Context-grounded datasets consist of question-and-answer pairs that reference source documents. Each example includes the query, relevant context from source files, and the correct response grounded in that context.
**Structure:**
* **Query** – The question or prompt
* **Context** – Relevant excerpts from source documents
* **Response** – Answer derived from the provided context
**Use cases:**
* Training models to use external knowledge bases effectively
* Teaching retrieval-aware response generation
* Building models that cite sources and stay grounded in provided information
These datasets are used with context-grounded fine-tuning to train models for retrieval-augmented generation workflows.
## Generation parameters
Dataset generation can be configured with parameters that control output characteristics:
* **Number of examples** – How many training pairs to generate from source content
* **Diversity settings** – Controls for question variety and coverage across source material
* **Quality filters** – Criteria for ensuring generated examples meet minimum standards
## Dataset quality
Generated datasets are optimized for training effectiveness:
* **Relevance** – Questions and answers are derived from actual source content
* **Consistency** – Output format matches fine-tuning requirements
* **Coverage** – Examples span the breadth of source material
* **Validation** – Generated datasets can be reviewed before use in training
## Dataset management
AI-ready datasets are managed alongside other data engine outputs:
* **Status tracking** – Monitor generation job progress
* **Review** – Inspect generated examples before fine-tuning
* **Versioning** – Maintain multiple dataset versions from the same sources
* **Export** – Download datasets in standard formats