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lawtonsolutions.bsky.social
Lawton Solutions
@lawtonsolutions.bsky.social
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99% of LLM users miss this:

Simulate an expert’s internal monologue.

Prompt example:

“You’re a senior engineer fixing a critical system failure. Think aloud.”

Result?

- Nuanced reasoning
- Tradeoff analysis
- Emotional weight
- Real logic flow

It’s not answers.
It’s how experts thi
99% don’t do this:

Prompt it to roleplay as a niche mentor.

Example:

“Act as a startup lawyer advising a solo founder building a fintech app in Europe.”

Now it thinks like an insider.

Better tips.
More nuance.
Real constraints.

Try:

- Veteran PM at Google
- Clinical trial desi
Most teams onboard new hires the hard way.

Here’s the smarter route:

1. Ask your LLM to build role-specific onboarding docs.

- Include tools, workflows, KPIs
- Add FAQs from past hires
- Match tone with company voice

2. Share it on day one.

Faster ramp. Fewer repeat questions. B
Most people grade creative work the wrong way.

Don’t guess. Customize.

Use an LLM to build your own evaluation rubric:

- Tell it your goals
- List what matters to you
- Ask it to create weighted criteria

Now you’re not just judging.
You’re measuring what you actually valu
Most prompt poorly.

Here’s one question that pulls hidden insights:

“What would a subject-matter expert notice here that a beginner would miss?”

Ask it after each response.

It forces depth.
Reveals gaps.
Surfaces nuance.

Try it—and watch your outputs shift from average to expert
Most people use LLMs to write.
Few use them to *judge*.

Try this:

Use an LLM to score proposals.

Feed in your criteria—
- Innovation
- Feasibility
- Fit for your org

Then let it rank and explain.

You’ll review 10x faster.
And surface best-fit ideas others miss.

Why not tr
Most onboarding processes waste time.

Fix this with LLMs:

1. Ask for:
- Role
- Location
- Tech stack

2. Prompt the LLM to build:
- Day 1–5 task lists
- Tool links
- Policy docs
- Setup checklists

3. Repeat for every hire

Zero HR bottlenecks.
Hiring slow?

Here’s a trick 99% miss:

Use LLMs to generate custom interview questions.

Give the model:
- Job description
- Resume
- Core skills needed

Ask for:
- Behavioral questions
- Skill-based drills
- Red flag detectors

Faster screening. Better hires. Less bias.

Try it?
99% of people miss this prompt.

Ask the LLM:

“What assumptions am I making in this argument?”

It spots:
- Hidden biases
- Gaps in logic
- Flawed framing

It’s like debugging your brain.

Want clearer thinking?

Let the model find what you're blind to.

Try it on your next draft.
Most LLM users write essays and emails.

But here’s something 99% skip:

Use LLMs to draft grant proposals.

Here’s how:

- Input your goals, timeline, budget
- Feed examples of past funded grants
- Add the review criteria

Refine until it matches what funders care about.

Most teams onboard new hires like it's 2004.

Fix it with this LLM tactic:

- List the role’s tools, tasks, workflows, and goals
- Ask the LLM to generate a detailed onboarding guide
- Review, tweak, and share with your new hire

Done in 15 mins.

Ramp time drops.
Clarity goes up
How to stress test your onboarding process with AI:

1. Tell the LLM:
"You just joined our team. Ask anything that seems unclear."

2. Feed it your docs, tools, SOPs, and wiki.

3. Watch it find:
- Confusing steps
- Missing links
- Gaps in process

You’ll spot blind spots fast.
99% of LLM users miss this:

You can simulate tools inside the chat.

Try these:

- “Act like a Linux terminal. Parse this bash script.”
- “Act like a spreadsheet. Walk me through this Excel formula.”
- “Act like an SQL shell. Draft this query.”

You’ll prototype faster. Debug smarter.
Most people ask LLMs for names like amateurs.

Do this instead:

1. Give context
- Brand tone
- Target audience
- Use case
- Industry

2. Ask for 10-15 names
- Ranked for style, clarity, and memorability

3. Iterate fast

Good naming = faster traction.

Tried this yet
Most people collect user feedback.

Then do nothing with it.

Here’s how to turn raw reviews into product gold with an LLM:

1. Paste survey + review text
2. Prompt:
- Group pain points
- Prioritize by volume + urgency
- Suggest features + fixes

You get a user-driven roadm
Most people test their product with *expected* inputs.

That’s a mistake.

Ask your LLM to simulate weird edge cases:

- Conflicting user actions
- Bad data inputs
- Rare user behavior
- Uncommon device settings

Stress it in ways you never imagined.

What fails when things
99% of LLM users miss this:

Auto-write onboarding emails from live user data.

Use:

- Sign-up source
- Behavior in first session
- Clickstream or survey data

Then prompt the LLM:

“Write an email for [type of user], highlighting [X features] in [tone].”

Engagement jumps.
Most people use LLMs for content or code.

But you can do this:

Describe your messy input →
Explain the target format →
Request a custom script.

LLMs will write:
- Regex
- Python
- Bash
- CSV transformers
- JSON normalizers

No-code automation unlocked.

What will
Most people translate content blindly.

Try this instead:

Use an LLM to build a multilingual glossary.

- Define key terms in your niche
- Translate them into target languages
- Reuse across all teams and content

You get:
- Consistency
- Accuracy
- Speed

Why haven’t
LLMs can train your hiring team.

99% don’t use this trick:

Generate fake but realistic interview responses.

Then use them to:
- Teach new recruiters
- Improve answer scoring
- Spot red flags faster

It’s low-cost, hands-on training.

Are you training future recruiters wi
99% of devs miss this LLM use:

Turn raw error logs into prioritized checklists.

Here's how:

- Paste the log
- Ask: “Create step-by-step actions to debug this”
- Get a clean list sorted by urgency or likelihood

Faster triage. Less guesswork.

Your debugger just got smarter.
99% of people don’t do this with LLMs:

Draft contracts.

Here’s how to start:

- Input key terms
- Specify conditions
- Add jurisdiction

Then have a lawyer review it.

You’ll save hours.
You’ll cut legal bills.
You’ll get better contracts faster.

Why aren’t you doing
Most people prep for interviews the hard way.

Instead, do this:

1. Paste the job description into an LLM
2. Ask: “Generate interview questions based on this description”
3. Then: “What would strong answers sound like?”

Now you’re training for *your* job—not just *any* job.

Most people stop after writing the perfect interview answer.

Wrong move.

Here’s how to stretch one response into 3 different formats:

- Conversational (for podcasts)
- Corporate (for press releases)
- Journalistic (for media quotes)

Same content. Different tone.

Stop wasti
99% of teams onboard the slow way: long docs, messy Slack threads, confusion.

Do this instead:

- Feed your LLM company docs
- Add role-specific inputs
- Generate custom onboarding guides

Every new hire gets a tailored playbook in minutes.

Faster ramp. Fewer questions. Better rete