On October 4, 2026, I plan to run the Cologne Half Marathon and beat my personal best of 1:55. This blog documents an experiment: can ChatGPT help me train more intelligently when it has access to the data and context from every completed session?
In the past, I followed standard training plans. They gave me structure, but they could not react to a bad night of sleep, a heatwave, an unexpectedly hard easy run or a session that went better than expected. This time, the plan is not fixed. After each run, I discuss the result with ChatGPT and use that conversation to decide what comes next.
The experiment
I am treating ChatGPT as a data-informed running coach. After every session, I share the available numbers, explain how the run felt and ask for an interpretation. The next recommendation is based on the accumulating record rather than a generic week from a standard plan.
I will try to follow the advice as closely as is reasonable. If I change or ignore a recommendation, I will document that as well. The interesting part is not a perfect success story. It is whether the process can make sensible decisions when training, recovery and everyday life do not line up neatly.
How the coaching process works
I record heart rate with a Polar upper-arm sensor and use an Apple Watch during the run. The activity is then transferred to Strava. From Strava, I share selected screenshots and values with ChatGPT.
The information can include distance, duration, average pace, kilometre splits, average and maximum heart rate, running power, elevation gain and the available charts. I also add information that is not visible in the activity file, especially perceived effort on a scale from 1 to 10, the weather, recovery and anything relevant about fuel or hydration.
This additional context was ChatGPT’s suggestion. It already matters because the first weeks of the experiment took place during very hot weather in Germany. A pace that feels easy at 18 degrees can tell a different story at 30 degrees.
My starting point
| Metric | Starting value |
|---|---|
| Age | 45 |
| Half marathon personal best | 1:55 |
| Recent 5K personal best | 26:54 |
| Estimated VO2max | 47.8 |
| Typical weekly volume | 30 to 35 km |
| Target race | Cologne Half Marathon |
| Race date | October 4, 2026 |
| Goal | A new personal best |
I also shared the heart-rate zones shown in my training app. They needed to be treated carefully because the stored resting heart rate of 75 bpm was outdated and higher than my current value. For that reason, the first assessment used pace, heart rate and perceived effort together instead of treating one automatically generated zone as the truth.
The first assessment
ChatGPT’s initial view was that basic speed was probably not the main obstacle. The recent 5K time suggested enough speed to improve on 1:55. The bigger question was whether I could hold an efficient pace for the full half marathon without losing form or slowing badly in the final kilometres.
The early priorities were therefore endurance, muscular durability and consistent easy mileage. Weekly volume could increase gradually if recovery remained good. Long runs would eventually reach 20 to 22 kilometres, while easy runs had to remain genuinely easy. The first working race target was 1:54, which is roughly 5:24 per kilometre.
The first draft of the plan
| Day | Session |
|---|---|
| Tuesday | 2 km warm-up, 6 x 800 m at 5:00 to 5:10 per km with 400 m jog recoveries, 2 km cool-down |
| Thursday | 8 km easy at approximately 6:10 to 6:30 per km |
| Saturday | 2 km warm-up, 6 km tempo at 5:35 to 5:40 per km, 2 km cool-down |
| Sunday | 15 km easy at approximately 6:10 to 6:30 per km, with an optional faster finish |
The wider plan also included mobility, easy cycling or walking and two strength sessions. Looking back at the first draft, it was ambitious. Two faster sessions, followed by a long run the next day, can create more fatigue than the individual workouts suggest. One purpose of the journal is to show whether the coaching process notices and corrects decisions like this.
How I will judge the result
The final race time matters, but it is not the only measure. I also want to know whether I can stay healthy, train consistently and recover well enough to complete the important sessions. I will watch whether pace improves at a comparable heart rate, whether long runs become easier and whether the advice responds sensibly to heat, fatigue and missed training.
Before the race, I will record ChatGPT’s final prediction. After the race, I can compare that prediction and the result with the decisions that led there.
The rules
- Every completed running session will be documented.
- Weather, perceived effort and relevant recovery information will be included.
- I will follow the advice when it appears reasonable and safe.
- If I change the plan, I will explain why.
- Poor sessions and mistakes will not be removed from the story.
- Medical advice and clear warning signs take priority over any coaching suggestion.
ChatGPT is not a certified running coach or a medical professional. I remain responsible for every training decision. This blog describes a personal experiment and is not individual training or medical advice.
Privacy and what I publish
Running data can reveal more than pace and heart rate. Exact routes, regular start locations and timestamps can expose where somebody lives or when they are usually away from home. I will therefore publish selected performance data, not a complete copy of my Strava account. (details on data flow)
I will not publish exact start or finish locations, identifying map details, other athletes or direct links to private activity data. Screenshots will be cropped, posts may be delayed and only health information that is relevant to the experiment will be included.
Why document it?
First, Codex makes it relatively easy to turn scattered workout notes into a structured journal. Second, the decisions may be useful to other runners who are curious about data-informed coaching. Third, this is also a practical test of how an AI agent handles a long project in which context accumulates and the next decision depends on what happened before.
What comes next
Each completed workout will get a shorter post with the session, the data, the interpretation and the next recommendation. The first test took place on July 14 in 30 degree heat. By October, I should have a much better answer to the question that started this project: can ChatGPT help me beat 1:55?
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