What the AI found
“Your post-lunch energy crashes coincide with meals containing more than 25g of saturated fat, impacting your perceived energy by 30-40% within two hours.”
Before
Vague fatigue, endless guesses
After
Clear energy insights, targeted changes
The same system, three states — real screens, not a screenshot
| Date | Meal — Saturated Fat (g) — Calories — Energy Score (1-10) |
| 2023-10-26 | Sandwich — 18 — 550 — 7 |
| 2023-10-26 | Curry — 32 — 780 — 4 |
| 2023-10-27 | Salad — 9 — 480 — 8 |
Prompt
Analyze this dietary and energy data. Identify any macronutrient or meal timing patterns that correlate with a drop in energy score greater than 2 points within two hours of a meal. Focus on saturated fat, carbohydrates, and protein.
Here's my data: [pasted spreadsheet data here]
AI
I've identified a recurring pattern. Meals containing more than 25g of saturated fat are consistently followed by a 30-40% drop in your perceived energy within two hours. This correlation is particularly strong for your post-lunch period, regardless of carbohydrate or protein content, or the exact meal timing within reason.5.2
Average Post-Lunch Energy Score (pre-AI)
7.8
Average Post-Lunch Energy Score (post-AI)
1/7
Days with Significant Post-Lunch Energy Drop (>2pts)
AI-Powered Energy Audit Reveals Hidden Drain
A data analyst uses AI to identify the precise timing and impact of a dietary factor on her daily energy levels, shifting from speculative fatigue to targeted dietary adjustments.
A 38-year-old data analyst, Northern Europe
Tools used
The real tools used here — swap any for your own equivalent. Each links to how we’d set it up.
Starting state
Before anything was set up
Our subject, a data analyst, felt perennially tired after lunch. She tracked much of her life – sleep, steps, diet – but the sheer volume of data across multiple apps made it impossible to connect any specific dots with her energy dips. She found herself cycling through speculative dietary changes, none of which delivered a clear improvement. The data was there, but the insight was missing, buried under layers of manual analysis she simply didn't have time for.
| Date | Meal — Saturated Fat (g) — Calories — Energy Score (1-10) |
| 2023-10-26 | Sandwich — 18 — 550 — 7 |
| 2023-10-26 | Curry — 32 — 780 — 4 |
| 2023-10-27 | Salad — 9 — 480 — 8 |
| 2023-10-27 | Pasta — 28 — 690 — 5 |
| 2023-10-28 | Soup — 12 — 400 — 9 |
Working state
All-Access, doing its job
Using a Google Sheet to collate her dietary intake with subjective energy ratings and meal timings, she then fed this structured data into Gemini. The prompt instructed the AI to look for correlations between specific macronutrient profiles, meal timings, and subsequent energy fluctuations. The AI's response, delivered in a clear and actionable summary, immediately highlighted a surprising pattern she had completely overlooked.
Prompt
Analyze this dietary and energy data. Identify any macronutrient or meal timing patterns that correlate with a drop in energy score greater than 2 points within two hours of a meal. Focus on saturated fat, carbohydrates, and protein.
Here's my data: [pasted spreadsheet data here]
AI
I've identified a recurring pattern. Meals containing more than 25g of saturated fat are consistently followed by a 30-40% drop in your perceived energy within two hours. This correlation is particularly strong for your post-lunch period, regardless of carbohydrate or protein content, or the exact meal timing within reason.Use case implemented
The finished system, running on its own
Armed with this specific insight, she adjusted her lunch choices to stay under the identified saturated fat threshold. A simple recurring calendar event now prompts her to review her energy scores and dietary intake each week in a simplified dashboard. The system now provides clear, actionable feedback, allowing her to make informed decisions about her diet and consistently maintain higher energy levels throughout her workday, without the previous guesswork.
5.2
Average Post-Lunch Energy Score (pre-AI)
7.8
Average Post-Lunch Energy Score (post-AI)
1/7
Days with Significant Post-Lunch Energy Drop (>2pts)
What an outside observer would notice
40%
Reduction in post-lunch energy dips
2.6 points
Average daily perceived energy increase
6 minutes
Time spent on diet analysis per week
The stack — build it yourself
Familiar, flexible, and handles structured data well for AI input.
Excels at nuanced data analysis from natural language prompts, ideal for identifying hidden correlations.
Simple, reliable for recurring prompts to maintain consistent review habits.
These are the tools used in this story. Any can be swapped for an equivalent you already trust.
Go deeper
Do this yourself
See All-Access
This story runs on All-Access. The tools and prompts above are the real build — swap any tool for your own equivalent and follow the same steps.