LinkedIn Content Strategy & Writing Style
Super practical product management and AI training
1 person tracking this creator on Viral Brain
Ed Biden positions himself as the bridge between high-level product strategy and the gritty reality of technical execution, specifically for leaders navigating the AI transition. His content strategy centers on closing the experience gap by moving beyond theoretical frameworks to advocate for "vibe coding," AI prototyping, and the construction of robust product operating models. He is notable for his "no-fluff" stance on leadership, often challenging executives to stop waiting for empowerment and instead build systems that channel CEO energy into commercial results. The most compelling intersection in his work is executive education meets rapid prototyping, where he transforms traditional C-suite coaching into a hands-on lab for shipping functional AI tools.
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Most product leaders have no idea if their teams are actually on track. They set the strategy. Hired the people. Kicked off the work. Then... silence until launch day. This is like building a print…
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5.8 posts/week
Posts / Week
1.3 days
Days Between Posts
1
Total Posts Analyzed
HIGH
Posting Frequency
56.6%
Avg Engagement Rate
STABLE
Performance Trend
340
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.77/10
Uniqueness Score
YES
Question Usage
0%
Response Rate
Writing style breakdown
<start of post>
Most teams ship AI features with no real idea if they’re getting better.
They ship a v1 prompt.
Users complain.
The team tweaks wording.
Then ships again.
And everyone calls that 'iteration'.
This is like trying to improve a recipe by changing the plating.
Sure, it looks different.
But you still don’t know if it tastes better.
AI quality is invisible until it suddenly isn’t.
A bad release doesn’t just underperform.
It embarrasses you in front of customers.
WHY THIS FAILS IN PRACTICE
Support tickets
NPS comments
Churn
It feels smarter now
By the time you see those, it’s already expensive.
And emotional.
THE FIX (A LIGHTWEIGHT OPERATING RHYTHM)
You don’t need a giant ML platform.
You need a simple loop that creates early signal.
𝟭. 𝗜𝗻𝘁𝗲𝗻𝘁 𝗥𝗲𝘃𝗶𝗲𝘄
One page.
What is this feature trying to do, for who, and in what context?
Define 3-5 failure modes in plain English.
If you can’t explain failure modes, you can’t test anything.
𝟮. 𝗘𝘃𝗮𝗹 𝗦𝗲𝘁𝘂𝗽
You need a tiny eval set before you ship.
20-50 real examples is enough to start.
Store them somewhere boring and durable (a spreadsheet is fine).
→ Include 'normal' cases
→ Include edge cases
→ Include the inputs that make your model hallucinate
This becomes your baseline.
Not perfect. Just real.
𝟯. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗥𝗲𝘃𝗶𝗲𝘄 (𝗽𝗿𝗲-𝘀𝗵𝗶𝗽)
Before you ship, run the eval set and review diffs together.
A prompt that got longer but worse
A model upgrade that quietly broke formatting
A “helpful” change that increases refusal rates
Cheapest intervention point in the cycle.
𝟰. 𝗜𝗺𝗽𝗮𝗰𝘁 𝗥𝗲𝘃𝗶𝗲𝘄 (𝟯𝟬 𝗱𝗮𝘆𝘀)
After launch, look at outcomes, not vibes.
→ Did the feature reduce time-to-complete?
→ Did it increase successful task completion?
→ Did it create new support burden?
If outcomes don’t move, stop polishing prompts and rethink the workflow.
NOTE: we’ll need to be connected so i can message you
This is true on LinkedIn and also in most orgs: access matters more than opinions.
TAKEAWAY
Prompting is not a quality strategy.
Evals are.
Not bureaucracy. Not theatre.
Just enough measurement to know if you’re improving.
ps: if you want the template, comment 'EVALS' and i’ll DM it
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<end of post>
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