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Documentation Index

Fetch the complete documentation index at: https://docs.axioniclabs.ai/llms.txt

Use this file to discover all available pages before exploring further.

Optimization is the primary workspace for inference-time control. Instead of switching between separate vector, sampling, and testing pages, you configure them together here around the currently selected model.

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What lives in Optimization

  • Selected model context so every control is scoped to the model you are actively tuning
  • Vector library and custom vectors in one place
  • Behavior-aware generation using SAE-monitored runtime controls
  • Sampling configuration for decoding strategy and method-specific parameters
  • Test generation to validate the active stack before using it in production
  • API snippet generation so the current configuration can be copied directly into application code

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Typical workflow

1

Select a model

Start with the trained or hosted model you want to tune.
2

Attach vectors or behaviors

Enable library vectors, your own vectors, or SAE-monitored behavior rules depending on what you are trying to achieve.
3

Tune decoding

Choose the sampling strategy and adjust only the controls relevant to that method.
4

Run test generations

Validate the current configuration before exporting it into an app or production workflow.
5

Copy the API snippet

Use the generated request example as the starting point for your client integration.

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Common use cases

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Safer or more constrained responses

  • attach a safety-oriented vector
  • lower creativity-oriented sampling
  • optionally enable SAE-monitored behaviors for drift correction

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Tone shaping

  • enable a persona or style vector
  • test different strength ranges
  • keep the underlying model unchanged

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Structured output

  • switch the decoding method to Guided Generation
  • choose one constraint type: JSON schema, Regex, or Grammar
  • guided generation depends on model/runtime support, so validate the behavior on the exact model you plan to ship
  • when using JSON schema, the UI checks that the schema is valid JSON before sending the request
  • the generated API snippet uses Axionic’s /sampling/generate endpoint with json_schema, regex_pattern, or grammar
  • for strict production formatting, prefer JSON schema or Regex; Grammar is better suited to lightweight format guidance
  • this advanced sampling path does not include the staged steering vector or behavior configuration shown elsewhere in the workspace
  • if your app uses an OpenAI SDK client, translate the same constraint into an inline policy passed through extra_body