Dataknobs Design Guide

Technical Report

Defining and Designing Knobs for Dataknobs Data Products

“Knobs” are the intentional control surface of a data product. A mature knob is not merely a variable; it is a controlled intervention point with telemetry, constraints, access control, auditability, lineage, and rollback.

This framing aligns with modern practice in feature flag standards (OpenFeature), observability standards (OpenTelemetry), and production release safety patterns (canarying).

What is a Knob?

A first-class, named control parameter that can be set intentionally, changes observable behavior in a documented way, is measurable end-to-end, and is fully governed.

Core Roles

Influencing Outputs

Altering inclusion criteria, ranking weights, thresholds, prompts, or decoding settings to shift results.

Enabling Experimentation

Formalizing "what changed" as an explicit treatment variable for A/B tests and causal learning.

Diagnostics & RCA

Isolating sensitivity and failure modes to determine if an issue is data, algorithmic, or context-based.

Governance & Risk

Tangible mechanisms to enact controls, such as least-privilege access, audit logging, and rollback.

Taxonomy of Knob Types

This taxonomy is artifact-derived: it identifies knobs by the domain objects you inspect and control.

Data Scope & Cohort Definition

Definition Controls which records are eligible (filters, segments, time windows).
Impact High leverage on output composition; bias profile risk.
Governance Access control, audit logs, lineage of cohort spec.
Example: "Only include transactions in last 30 days"

Data Representation & Aggregation

Definition Summarization, granularity, windowing, deduplication strategies.
Impact Medium-to-high; changes signal-to-noise and ranking outputs.
Governance Version representation specs; lineage at feature level.
Example: "Use 7-day vs 28-day rolling window"

Data Integrity & Quality Gates

Definition Thresholds that determine if inputs are "good enough" (nulls, drift).
Impact Indirect but critical; prevents silent degradation.
Governance Audit event logging; treat gate changes as controlled changes.
Example: "Block if null_rate > 0.5%"

Data Semantics & Label Definition

Definition Controls the meaning of "truth" or "success" (attribution windows).
Impact Very high; redefines optimization objective.
Governance Controlled change management; lineage from label spec.
Example: "Conversion within 7 days vs 30 days"

Comparison Matrix

Knob Type Observability Risk Level Governance
Data Scope Medium High High
Data Quality Gates High Medium High
Feature Flags High Med-High Med-High
Model Selection High High High
Safety Guardrails High High High

Discovery Heuristics

How to identify candidate knobs in your system.

From Data

  • Sensitivity: Features that drive errors or have nonlinear effects.
  • Causal Inference: Variables that act as actionable treatments, not just attributes.
  • Drift: Fields with frequent drift need gating or fallback triggers.

From Code

  • Magic Numbers: Hard-coded thresholds that encode business policy.
  • Feature Flags: Existing toggles that need formal lifecycle management.
  • Observability: Areas where ops teams frequently wish for controls during incidents.

From Models

  • API Params: Temperature, top_p, and generation strategies.
  • Prompts: System instructions and tool definitions.
  • RAG: Retrieval chunk size, K-nearest neighbors, and filters.

Selection Flows

Deciding whether a knob is for Governance (safety) or Experimentation (learning).

Data Domain Logic

1

Does it redefine cohort/scope or labels?

Yes → Classify as Governance-first (Requires approvals, lineage, rollback)
2

Is there recurring data incidents or drift?

Yes → Classify as Hybrid (Gate + staged rollout)
3

Is output sensitivity high and measurable?

Yes → Classify as Experimentation-first (A/B test or offline eval)

AI Domain Logic

1

Does it affect safety or sensitive data?

Yes → Classify as Governance-first (Policy-as-code + Strict Audit)
2

Does it change model version/routing?

Yes → Classify as Hybrid (Registry + Canary)
3

Is it decoding or prompt tuning?

Yes → Classify as Experimentation-first (Offline eval + A/B)

Operating Safely

The "Knob Maturity" Rubric

A knob is production-grade only when it satisfies these four pillars:

1. Definition

Has a name, intent, valid range, default value, and defined ownership ("who can change it").

2. Measurement

Telemetry records effective values and links them to outputs (Traces/Metrics/Logs).

3. Safety

Staged rollout (canary/flags), clear rollback path, and documented failure modes.

4. Governance

Access control (least privilege), audit logging, and lineage to artifacts/runs.