Allie K. Miller recently shared something that caught my attention: "Just had Claude Code build me a full health projection based on one year's worth of daily scale data." She went on to describe having "hundreds of data points" on weight, body water, skeletal muscle, metabolic age, and more, then turning a batch of screenshots into customized projections and recommendations "in minutes."

What resonated most was not the novelty of chart-reading. It was her blunt takeaway: "Tracking more of your health and context is a massive upgrade for your AI use." And then the punchline that a lot of us need to hear: "If you haven't already, BUILD A DAMN CONTEXT VAULT FOR CLAUDE."

I want to expand on what Allie is pointing at here, because it's bigger than health tracking. It's an operating model for getting consistently high-quality output from AI: pair better personal data with a reusable, structured context vault so the model stops guessing who you are.

The real lesson: AI is only as personal as your context

Allie's workflow is a perfect example of the gap between generic AI advice and actually useful AI.

Most people prompt like this:

"Analyze my weight trend and give advice."

The AI responds with broad, safe guidance because it lacks specifics, constraints, goals, and history.

Allie prompted like someone who understands systems. She essentially said (paraphrasing her post): look at my latest files, also access my personal and business context docs, project 13 variables, identify strengths and weaknesses, and make recommendations custom to my context.

That last phrase is doing the heavy lifting: "custom to my context." When you give AI durable context plus fresh data, you unlock analysis that feels closer to an ongoing relationship than a one-off query.

Why screenshots and "messy" inputs still work

One subtle point in Allie's post is that she didn't do a pristine data export. She "took 14 screenshots of the graphs" and dropped them into Claude Code.

That matters for two reasons:

It lowers the barrier to entry. If your health data is trapped in an app, screenshots are the quickest bridge.
Modern multimodal models are good enough to extract trend signals from visual charts, especially if you guide them on what to look for.

If you're waiting to build the perfect pipeline, you might never start. Allie's approach is pragmatic: start with what's available, then improve the system over time.

What a "context vault" actually is (in practice)

A context vault is a set of documents that capture stable, high-signal information about you so you do not have to reteach it every session.

Think of it like a personal API for your life.

At minimum, it should include:

Personal baseline: age range, general health status, allergies or restrictions, typical schedule, travel frequency
Goals: what you are optimizing for (fat loss, muscle gain, sleep, endurance, stress reduction)
Preferences: foods you will actually eat, workouts you will actually do, budget constraints
Non-negotiables: injuries, medical constraints, time constraints, equipment constraints
Decision rules: how you like recommendations delivered (simple checklist, detailed rationale, weekly plan)

Allie mentions having a "personal life context doc" and a "business life context doc." That separation is smart. Different domains need different assumptions, and it reduces irrelevant spillover.

"Build context docs for different aspects of your life. Saves me multiple hours every week teaching AI who I am."

That is the real ROI. Context vaults are not about fancy prompting. They are about eliminating repeated setup cost.

A simple structure for your own context vault

If you want to copy the spirit of Allie's system, here is a structure that tends to work well without becoming a second job.

H3: Start with 4 core documents

"About Me" (1-2 pages)
- Bio-style overview, current season of life, energy patterns, constraints
"Goals and Metrics"
- 3-5 goals, how you measure them, what a win looks like in 30/90/180 days
"Rules and Preferences"
- Food, exercise, communication style, risk tolerance, what you hate doing
"Current Projects and Schedule"
- Weekly cadence, upcoming events, typical work blocks, travel, stress points

H3: Add a domain pack (health, business, learning)

For health specifically, add:

Devices and data sources (smart scale, Oura, Apple Health, Garmin, labs)
Definitions (what the app's "metabolic age" means to you, or how you treat it)
Known confounders (cycle, hydration swings, high-sodium meals, creatine use)
Training plan history (what you've tried, what worked, what caused injury)

The goal is not to over-document. The goal is to capture the 20 percent of context that improves 80 percent of outputs.

Turning health tracking into projections (without fooling yourself)

Allie asked Claude Code to "build out full projections for all 13 variables" and "lay out all strengths and weaknesses." That is incredibly useful if you treat it as directional guidance, not medical truth.

Here are the kinds of projections an AI can do well when given a year of daily measurements:

Trend smoothing: separating noise (daily water shifts) from signal (long-term change)
Seasonality detection: weekends vs weekdays, travel weeks, holiday patterns
Correlation hints: when weight spikes align with low sleep or high stress weeks
Plateau diagnostics: identifying when behaviors likely changed

Where you should be cautious:

Over-interpreting scale-derived metrics (body water, muscle mass estimates)
Confusing correlation with causation
Treating "metabolic age" as a clinically actionable metric

A good prompt asks the model to express uncertainty.

For example, add:

"State confidence levels for each variable and why."
"List top confounders that could distort this metric."
"Offer 2-3 hypotheses for each trend and how to test them."

The prompt pattern Allie used (and why it works)

Allie's prompt works because it combines:

Data (the screenshots)
Retrieval ("look at the 14 most recent files")
Persistent context (personal and business docs)
Multi-output deliverables (projections, strengths, weaknesses, recommendations)

If you want a reusable template inspired by her approach, try:

Inputs: "Use the latest files in folder X" (or attach screenshots)
Context: "Also reference my context vault docs A, B, C"
Outputs:
- "Summarize trends"
- "Project next 30/90 days"
- "Identify leverage points"
- "Recommend actions customized to my constraints"
Guardrails:
- "Do not give medical diagnosis"
- "Ask 5 clarifying questions if needed"

This is what makes AI feel like a real assistant rather than a generic content generator.

Why this is also a LinkedIn content strategy lesson

There is a reason Allie's post took off. It has the classic viral posts structure:

A concrete win (health projection in minutes)
Specific numbers (one year, 14 screenshots, 13 variables)
A repeatable method (context docs)
A strong opinion stated plainly (build a context vault)

If you create LinkedIn content, notice how the story is both personal and transferable. That is the sweet spot for viral posts: specific enough to be believable, general enough to be useful.

The actionable takeaway: build the vault, then feed it signals

If you do one thing after reading Allie's post, do this:

Create a "Context Vault" folder.
Write a one-page personal context doc and one-page work context doc.
Add one domain pack (health) if you track metrics.
The next time you ask AI for advice, reference the vault explicitly.

As Allie K. Miller put it, this saves you from "teaching AI who I am" every week. And once you feel that compounding effect, it is hard to go back.

This blog post expands on a viral LinkedIn post by Allie K. Miller, #1 Most Followed Voice in AI Business (2M) | Former Amazon, IBM | Fortune 500 AI and Startup Advisor, Public Speaker | @alliekmiller on Instagram, X, TikTok | AI-First Course with 300K+ students - Link in Bio. View the original LinkedIn post →