Lou Adler recently shared something that caught my attention: "We just created a living library of prompts you can use to redesign your current job using AI" and "as you redesign your job stay keep prompting the AI or GPT without leaving the chat." That simple combination - a practical prompt library plus the reminder to keep the conversation going - is the kind of advice that sounds obvious after you hear it, but changes the quality of results when you actually apply it.

In this post, I want to expand on Lou’s idea and turn it into a repeatable approach you can use whether you are a recruiter, hiring manager, or individual contributor trying to shape your role into something more impactful.

Key insight: You do not get the best output from a single prompt. You get it from a sequence of prompts that build on each other in the same chat.

Why "job redesign" matters more than ever

When people hear "redesign your job," they often think of a major re-org or a formal change to a job description. In practice, job redesign can be much smaller and more immediate:

Clarifying what outcomes the role should drive
Dropping low-value tasks that create noise but not results
Automating repetitive work
Rebalancing time between deep work, collaboration, and customer-facing activities
Resetting expectations with your manager or stakeholders

AI helps because it can function like an always-available analyst and editor. But it only works well if you provide context and keep iterating.

Lou Adler’s prompt library idea: treat prompts like tools, not tricks

Lou points to a "living library" of prompts you can use to redesign your current job (he shared it here: https://lnkd.in/gqkSaQ3K). The phrase "living library" matters. The best prompt sets are not static. They evolve as:

Your role changes
Your company strategy changes
The market changes
The AI tools improve

If you are in hiring or HR, this mindset is even more important. Candidate expectations, skill requirements, and compensation dynamics shift quickly. A prompt that worked six months ago may still be useful, but it likely needs tuning.

What a good prompt library should cover

If I were building on Lou’s hiring tool kit framing, I would organize prompts into categories that map to real workflows:

Role definition and success outcomes
Task inventory and automation opportunities
Stakeholder alignment and communication
Career growth and skill development
Hiring and assessment design (for managers and recruiters)

A library is most valuable when you can quickly pick a prompt that matches the problem you actually have today.

The most important line in the post: stay in the same chat

Lou writes: "stay keep prompting the AI or GPT without leaving the chat." This is a tactical instruction with huge impact.

Here is why it works:

The AI retains the context from earlier messages, so you do not have to re-explain everything.
Your later prompts can reference earlier outputs: "Use the scorecard you just drafted" or "Rewrite the role outcomes in fewer words."
You can progressively add constraints, examples, and preferences, which tightens the output.

Some people call this "chain-of-thought prompting." Regardless of the label, the practical takeaway is simple: treat it like an ongoing working session, not a one-off search query.

Key insight: The first answer is rarely the best answer. The best answer is the one you refine through iteration.

A practical workflow: redesign your job in one AI session

Below is a structured conversation you can run in a single chat thread. This expands on Lou’s concept and turns it into a mini playbook.

Step 1: Start with outcomes, not tasks

Prompt

"I want to redesign my job. Here is my current role, team, and goals: [paste]. Ask me 10 questions to clarify the outcomes that define success in the next 6 months."

Why it works

Most roles accumulate tasks over time. AI can help you reverse-engineer what outcomes matter, then evaluate tasks against those outcomes.

Step 2: Inventory your work and label value

Prompt

"Based on my answers, generate a table of my recurring tasks with columns: frequency, time spent, business value, risk if dropped, and automation potential."

Follow-up prompt

"For each task, suggest: eliminate, automate, delegate, or keep. Explain in 1-2 sentences."

Why it works

You get a decision framework, not just a list. This is the difference between "being busy" and "being effective."

Step 3: Identify leverage points for AI automation

Prompt

"For the tasks labeled automate, propose specific AI-assisted workflows and example prompts. Include quality checks to avoid errors."

Follow-up prompt

"Assume I can only automate 2 tasks this month. Which two should I pick for the highest ROI and why?"

Why it works

It forces prioritization. AI can propose a dozen automations, but you need the two that materially change your week.

Step 4: Redesign the role narrative (this helps careers and hiring)

Whether you are pitching this to your manager or rewriting the role for a job posting, you need a clear narrative.

Prompt

"Rewrite my role description to focus on outcomes, scope, and measurable impact. Keep it to 6 bullet points."

Follow-up prompt

"Now create a 30-60-90 day plan aligned to those outcomes."

Why it works

This is where job redesign becomes legible to other humans. It is also where hiring gets better because you shift from vague requirements to measurable performance.

Step 5: Turn the redesign into a hiring scorecard (if you manage people)

Lou Adler’s broader body of work emphasizes performance-based hiring. AI can help you create scorecards that reflect real work.

Prompt

"Using the outcomes above, build a hiring scorecard with 5-7 performance objectives, each with measurable evidence and interview questions to validate them."

Follow-up prompt

"Identify the top 3 false-positive signals we should avoid in interviews for this role."

Why it works

It pushes you away from credential shopping and toward evidence of capability.

Common mistakes to avoid when using AI for job redesign

Even with a strong prompt library, a few pitfalls show up repeatedly:

Not providing enough context
If you do not share constraints (tools, team size, industry, deadlines), you will get generic advice.
Accepting the first draft
Treat output as a starting point. Ask for revisions, alternatives, and tighter language.
Mixing too many goals
Job redesign, promotion planning, and a job search can overlap, but try to focus each chat session on one primary outcome.
Forgetting the human alignment step
AI can draft the plan, but you still need buy-in from your manager, your team, or your stakeholders.

A simple way to contribute back, like Lou invited

Lou also asked people to "comment below with how well they worked" and whether they would like to add prompts to the tool kit. That community feedback loop is exactly how a prompt library becomes useful over time.

If you build your own mini set of prompts, consider saving:

The initial context you pasted (role, goals, constraints)
The best follow-up questions the AI asked you
The final outputs you actually used (scorecard, 30-60-90, task automation plan)

Over time, you will have your own living library tailored to your work, and you can share the best parts with your team.

Closing thought

What I like about Lou Adler’s post is that it is not hype. It is a practical invitation: use a prompt library to redesign your job, and keep the conversation going in the same chat so the results compound. If you do that consistently, AI stops being a novelty and starts behaving like a real work partner that helps you clarify, prioritize, and execute.

This blog post expands on a viral LinkedIn post by Lou Adler, CEO, Performance-based Hiring Learning Systems. Author, Hire with Your Head and The Essential Guide for Hiring.. View the original LinkedIn post →