Prompt Template Specification

A Prompt resource defines a format-string template that maps dataset feature columns into structured training input. Prompts bind a dataset’s column names to positional slots in the training text, controlling how raw data is presented to the model during fine-tuning and evaluation.

Source: ft/prompts.py, ft/utils.py, ft/jobs.py, ft/db/model.py

Template Fields

Placeholders use Python format-string syntax: {feature_name}. Each placeholder must correspond to a column name in the linked dataset’s features JSON array.

ORM Schema

class Prompt(Base, MappedProtobuf, MappedDict):
    __tablename__ = "prompts"
    id = Column(String, primary_key=True)               # UUID
    type = Column(String)                                # PromptType enum value
    name = Column(String)                                # Display name (unique)
    description = Column(String)
    dataset_id = Column(String, ForeignKey('datasets.id'))  # Linked dataset FK
    prompt_template = Column(String)                     # Full template
    input_template = Column(String)                      # Input portion
    completion_template = Column(String)                  # Output portion

Import Validation

_validate_add_prompt_request() enforces:

1. Required fields: id, name, dataset_id, prompt_template, input_template, completion_template must all be present on the PromptMetadata message.
2. Non-blank name: name.strip() must be non-empty.
3. Unique name: No existing prompt may share the same name.

The prompt is created via Prompt.from_message(request.prompt), which uses the MappedProtobuf.from_message() method to map protobuf fields directly to ORM columns.

Auto-Generation from Dataset Columns

ft/utils.py::generate_templates(columns) produces default templates from a list of dataset column names:

1. Output column detection: Compares column names against a ranked list of 500 common output column names (e.g., answer, response, output, label, target). The column matching the highest-ranked name becomes the output column. If no match, the last column is used.

2. Input columns: All columns except the identified output column.

3. Prompt template: Generated as:

   You are an LLM responsible for generating a response. Please provide a response given the user input below.
   
   <Column1>: {column1}
   <Column2>: {column2}
   <Output>:
   

4. Completion template: {output_column}\n

Returns (prompt_template, completion_template).

Axolotl Auto-Prompt

ft/jobs.py::_add_prompt_for_dataset() generates a prompt automatically when using the Axolotl framework and no prompt is provided:

1. Load the Axolotl config from the database by axolotl_config_id.
2. Parse the YAML config and extract the dataset type from config['datasets'][0]['type'].
3. Query the Config table for a matching axolotl_dataset_formats config by description == dataset_type.
4. Parse the format config JSON to extract feature column names.
5. Build a template: <Feature>: {feature}\n for each feature.
6. Check for an existing prompt with the same dataset_id and prompt_template. If found, return its ID.
7. Otherwise, create a new Prompt named "AXOLOTL_AUTOGENERATED : {dataset_type}_{dataset_name}".

Removal

remove_prompt() deletes the Prompt record by ID. Note that prompts are also cascade-deleted when their parent dataset is removed with remove_prompts=True.

Protobuf Message

PromptMetadata fields: id, type, name, description, dataset_id, prompt_template, input_template, completion_template.