Drift GPT-AI drift training & tuning

Example

A modular course such as "First Gear: Starting Your Drift Adventure" broken into chapters: (1) Mindset & safety, (2) Vehicle controls & orientation, (3) Entry techniques & weight transfer, (checkpoint: learner submits performance evidence), (4) Sustained drift transitions, (5) Linkups and consistency. Each module bundles short theory, a 3-step drill, a micro-quiz and a practice template.

Scenario

Novice driver completes chapters 1–3 at home using theory and simulator practice. Before Chapter 4, Drift GPT requires a checkpoint (the student uploads a short video or answers a targeted quiz). After review, the assistant provides prioritized corrections (e.g., 'reduce steering snap at 0.8s, delay throttle by ~0.3s') then unlocks intermediate drills. The interaction prevents skipping critical foundational skills and personalizes the advice to the learner's observed performance.

Example

Input: car mass, tire diameter, engine torque curve or power band, current final drive, intended track type (tight technical vs high-speed bank), and driver style (fast transitions vs long slides). Output: prioritized tuning checklist that includes recommended final-drive direction (shorter vs taller), suggested gear spacing strategies to keep shift points in the engine’s peak torque/power window, a suspension baseline table (spring rate range, damper ride/bleed direction), and differential options (limited slip clutch preload range or torque bias suggestions).

Scenario

A club racer wants a gearbox strategy for a short, technical layout. They submit: 1,200 kg car, 300 hp with peak power 5,500–7,500 rpm, 17" wheels. Drift GPT explains tradeoffs (shorter final drive for quicker recoveries but less top speed), suggests a gear-ratio philosophy to ensure 1st–4th keep the engine in the 5.5–7.5k band for typical shift points, proposes a diff configuration that emphasizes controllable breakaway and progressive lockup for transitions, and provides a tuning session plan: test 3 final drive options, measure 0–100m shift recovery times, and iterate. (Drift GPT frames numbers as starting points and instructs testing under controlled conditions.)

Example

Generates: (a) telemetry CSV templates with headers (time, speed, RPM, throttle %, brake %, steering angle, yaw rate, lateral accel, GPS), (b) mock telemetry graphs for target vs actual lines, (c) drill sequences with repetitions, rest intervals and target metrics, (d) instructor cue cards and scoring rubrics.

Scenario

An advanced driver wants to improve mid-corner transition angle. Drift GPT produces a four-session plan: Session A (30 controlled entries, focus on clutch timing), Session B (6 sets of progressive speed entries with telemetry capture), Session C (data review: highlight peak yaw and slip angle windows), Session D (consolidation and coach feedback loop). For each session it supplies expected telemetry markers to watch and a post-session checklist (camera placement, telemetry sync, safety reminders).