3.1.1 - Why Sharing Early Creates the Learning Your Build Needs

Plan the early sharing plan: say who will see it, what they will see and what question their reaction needs to answer.

Are you sharing early enough to learn something useful?

The call

Share before it feels ready. Otherwise you launch with assumptions that could have been tested weeks earlier.

This is a Launch idea, so there are two AI moves: first Sharpen the early sharing plan using the prompt below, then Build it with AI, building carefully to carry the decision into the live system. This idea also calls for Run it for real.

New here? Let your AI do this with you. Paste your problem into Guide me and it builds a prompt that walks you through this in ChatGPT or Claude. You do not need to know how to prompt.

Prefer to drive the AI yourself? Give ChatGPT or Claude this page and ask it to help. For skill.txt, the Claude Code plugin and other ways in, see how to use vibe2value.

Or copy this prompt into AI chat, replace the bracketed lines with your real early sharing plan and keep the instruction exactly as visible here. It helps you put your launch/early-sharing-plan.md together by refining your three lines until two people would make the same product decision from them.

You are checking whether this early sharing plan is clear enough before you move forward.

Constraint:
The early sharing plan must be specific enough that two people would run the same learning loop from it.

Example of the standard:
Vague: "We should share the search tool early and get feedback."
Sharp: "Show the context-shaped search to five content creators who publish weekly and ask whether the results feel more relevant than what they get from a generic search tool."
The sharp version is specific enough that two people would run the same learning loop. The vague one is not.

Working draft:
Audience: [who will see it]
What they will see: [what they will see]
Question to answer: [what question their reaction needs to answer]

Task:
Decide whether this early sharing plan is specific enough that two people would run the same learning loop. If it already is, say so. If it is vague, rewrite it to meet the standard in the example above.

Check:
- Would two people interpret this the same way?
- Does it stay concrete enough to guide the next step?
- Can you state who will see it, what they will see and what question their reaction needs to answer?

Return:
- Verdict: clear, or needs work
- The corrected three lines, or the original three if already clear
- What was vague, and the one change that fixed it

Copy this into AI chat. Replace the bracketed parts. Keep the rest unchanged. AI will likely suggest refinements based on what you enter. Use those to sharpen your thinking, not replace it.

There are six ways to work with AI across a build. This idea's AI moves are noted above. See Working with AI for all six and where each fits.

The same idea on a recipe card

Picture asking someone to taste the sauce while you are still at the stove: "try this, does it need more salt?" You can still fix it. Wait until the full dinner is plated and served before you find out it was bland, and there is nothing left to do but apologise.

Sharing early is that spoonful held out mid-cook. Pick one person to taste, one thing to show them and the one question you need answered, before the dish is finished. You share to learn while you can still change it, not to collect applause once the plates are already down.

The recipe card is just a simple example, using everyday cooking ideas everyone understands, to make the concept clear. See the recipe card.

What it really means

Sharing early is not just posting work sooner. It is choosing who should see what and what question that exposure is meant to answer. Until you can name one audience, one thing to show and one question you need answered, early sharing turns into noise. AI can help package the work, but it cannot decide what learning matters.

Compare the broad version with a version you can actually test.

Too vague: We should share the search tool early and get feedback.
Concrete enough to test: Show the context-shaped search to five content creators who publish weekly and ask whether the results feel more relevant than what they get from a generic search tool.

The second version lets two people run the same learning loop from it.

Sharing early creates a loop: a rough build goes to a few real users and what you learn comes straight back while you can still act on it. Share late and the build is finished before the learning arrives, when it is too expensive to use.

Pass: You can say who will see it, what they will see and what question their reaction needs to answer.
Fail: If sharing early still means putting it in front of people and seeing what happens, it is not clear enough yet.

Do not move into outreach, launch or feedback collection until this passes.

What you'll walk away with

This post is about the framing decision: the words that pin down what this idea actually means for your build, before any code. You'll come out with your own knowledge-base/launch/early-sharing-plan.md written and sharpened: the early sharing plan pinned down as a decision, three worked examples to map against your own surface and an AI prompt that pressure-tests it until two people would make the same call.

Write it down

Your early-sharing-plan.md is a real file in your project. The AI forgets everything between sessions. It reads this file each time to pick up what you already decided, on this idea or another part of the build. Write it down once instead of explaining it again.

knowledge-base/shape-build-launch/launch/early-sharing-plan.md is just a suggested name and place for this information. The name and the location are just how we organise this type of documentation, not something you have to follow. Call the file and put it wherever suits your project. What matters is that it is written down in a format that is easily understood by both people and the AI.

The .md ending is markdown, a plain text format often used for documents kept in a code repository like GitHub. It is just the common way people store this kind of writing alongside their code.

Writing it down is how you keep good context for your AI. See Keep the signal, not the noise for why this matters across a whole build.

Risk and mitigation

Risk: Sharing early without a clear intent, which invites broad opinions that slow launch choices and hide real user friction.
Mitigation: Define one learning question for each share and only act on feedback tied to observable behaviour.

Key takeaway

Do not move forward until you can say who will see it, what they will see and what question their reaction needs to answer.

How to document your early sharing plan

Write your early sharing plan up in full so your AI has the whole picture: the answer, why you believe it, what you are optimising for, where you might be wrong and the standing instruction it should follow. Keep it in a file with your project like this.

# Early sharing plan

## Answer
Audience: [who will see it]
What they will see: [what they will see]
Question to answer: [what question their reaction needs to answer]

## Evidence
Why you believe this. Conversations, examples, tickets, your own experience.

## Decision
What you are optimising for and what you are saying no to.

## Risk
Where you might be wrong and what would tell you.

## AI instruction
The standing note your AI reads on this project.

Here is one filled in so you can see what good looks like, grounded in the recipe card idea from earlier.

## Answer
Audience: Someone in the kitchen with you
What they will see: A spoonful of the sauce while it is still on the stove
Question to answer: Does it need more salt

## Evidence
When I have let someone taste mid-cook they have caught an underseasoned sauce while I could still fix it.

## Decision
Optimising for one quick taste while it is still on the stove. Saying no to waiting until it is plated to ask.

## Risk
A single taster's palate might not match the table's. Take it as one signal, not the final word.

## AI instruction
Share a spoonful of the sauce with someone in the kitchen while it is still cooking. Ask one question, does it need more salt, and use the answer while there is time to change it.

What now?

Apply it. Guide me turns what you are building into a prompt that walks you through this idea.

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Early sharing plan

The call

Sharpen the early sharing plan with AI