Glossary
Feedback Loop
Updated February 28, 2026
Your feedback loop is the process where your logs become the model input that reshapes your next recommendations.
How the loop runs
Unfair uses a fixed reporting cadence of log data points, not a single reaction:
- Input stage: a dose entry, missed dose, symptom note, or override is timestamped and stored.
- Signal stage: timing regularity, safety flags, and response notes are translated into trend signals.
- Rank stage: recommendations are recalculated by adjusting relevant ingredient scores and confidence bands.
- Review stage: the app surfaces why suggestions changed and what changed next.
When there are new logs, your recommendations can shift after a short rolling window.
For most users, meaningful updates show after multiple points within the same 24–72 hour period rather than one isolated entry.
One miss vs repeated misses
- One missed dose: usually creates a minor confidence reduction and may add “confidence dip” messaging for the same stack.
- Repeated misses: increases uncertainty in signal quality and can lower ranking weight for that ingredient or time-window recommendation.
- Pattern of misses + ongoing adverse symptom notes: may trigger safety-first downgrades and pause prompts until risk is reviewed.
Minimum data needed for adaptation
Unfair needs at least three of these in a usable cadence to adapt a stack:
- dose entries for at least three scheduled times
- adherence status for a majority of those times
- at least one symptom or energy/sleep/journal outcome entry per two-day window
- one consistency marker such as wake-time, meal context, or reminder state
If data is sparse, the rank engine keeps a conservative stance and favors previously stable recommendations.
Practical action step
Before expecting a major change, complete 7 days with complete entries before 10pm local time and keep dosing windows stable.
This gives the loop enough signal to separate context noise from real effects.
Uncertainty and limits
- Evidence is limited on how quickly the loop should adapt to major context shifts like travel, sleep schedule collapse, or acute illness.
- Evidence is limited on the exact point where missed-dose noise becomes stable enough to reduce ranking swings.
Cross-site references
How this appears in Unfair
Unfair shows the loop as confidence updates in the rationale, reminder timing suggestions, and rank changes.
If signal quality is thin, it will mark recommendations as “low confidence” and ask for more complete logs.
Clinical safety note
If repeated missed doses coincide with neurologic, cardiovascular, or GI concerns, treat recommendations as provisional and ask for clinician input before intensifying or stacking further.