Glossary
AI Diet Planning
Updated February 28, 2026
AI Diet Planning builds meals by combining goals, constraints, and behavioral context into a reliable weekly plan.
Planning inputs with constraints
AI planning quality depends on input completeness.
| Input group | Why it matters | Example format |
|---|---|---|
| Target state | current phase and acceptable trend | fat loss, maintenance, recomposition |
| Medical and allergy flags | safety and substitution confidence | shellfish allergy, lactose intolerance |
| Kitchen and tools | feasibility and meal variety | oven, blender, slow cooker, air fryer |
| Budget band | ingredient ceilings and store routing | strict budget, standard budget, flexible |
| Prep rhythm | adherence realism | 20 min, 30 min, 60 min windows |
| Schedule | training timing and shift pressure | early shifts, late workouts, travel days |
| Preference boundaries | diet style and taste constraints | vegetarian, no red meat, spice tolerance |
Fat-loss flow versus maintenance flow
The planning engine uses the same architecture, then swaps priorities per objective.
| Flow stage | Fat-loss mode | Maintenance mode |
|---|---|---|
| First pass | define large satiety anchors and tight calorie ceiling | set flexible calorie floor and protein floor |
| Composition | lower energy density with equal micro quality | balance preference stability and consistency |
| Swap policy | prefer vegetables, lean protein, and high-volume starch anchors | preserve preferred foods first, then optimize timing |
| Review window | weekly trend and hunger review | bi-weekly trend with adherence review |
| Risk control | cap fast reductions and avoid repeated food debt days | avoid unnecessary strictness during stress events |
Plan generation guardrails
AI Diet Planning runs three mandatory checks before final output.
| Guardrail | Check |
|---|---|
| Allergen safety | block allergen items and rank alternatives |
| Feasibility | remove meals that exceed equipment or prep limits |
| Budget continuity | compare grocery basket with budget band before finalizing |
Recalibration template for plateau
Plateaus are handled with a minimal-change protocol before major direction shifts.
- Confirm logging density and weigh-in consistency for 7 to 10 days.
- If adherence is below 80%, keep the plan and improve execution first.
- If adherence is stable and trend is flat, adjust one lever only: calories, meal timing, or portion density.
- Re-run the same week structure and check whether the objective band is restored within the next 14 days.
Recalibration template for non-adherence
Non-adherence gets a recovery-first response, not a punishment loop.
- Detect missed meals, skipped checks, or repeated no-shows in the log.
- Open a simpler plan version with fewer ingredients and lower setup cost.
- Re-anchor macros around the same protein core and stable sides.
- Resume normal complexity only after 3 to 5 reliable days.
Reliability drop and fallback logic
Plan reliability drops when the confidence between user behavior and model assumptions narrows.
| Reliability drop signal | Immediate fallback |
|---|---|
| Repeated mismatches on two consecutive plan cycles | switch to a constrained base template |
| Frequent meal swaps outside model confidence | lock a core menu for 48 to 72 hours |
| Erratic adherence and low data quality | pause auto-adjustments and request manual review |
When fallback activates, AI Diet Planning keeps recommendations understandable, practical, and low-friction until confidence returns.
For related setup behavior, use meal planning, and macro-friendly recipes for swap quality.
When plan quality drifts, pair this page with adaptive calorie goals and plan reliability workflows.