I’ve just seen a very interesting framework for creating good AI. The CLEAR system, as explained by Nick Saraev.

For me, it ticks a lot of boxes that I find useful. It is concise, useful, and portable.

Let’s review it:

C — Clarity

Define the problem precisely with measurable outcomes.

• ❌ Bad: “Build me a lead gen system.”

• ✅ Good: “Create a one-page lead qualification SOP for companies with 50+ employees in manufacturing that expressed interest in automation in the last 90 days.”

🔑 Tip: Always specify who, what, when, where, how many.

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L — Logic

Break the task into clear, sequential steps with decision points.

Example: Lead scoring system

1. Collect leads from LinkedIn ads + cold email.

2. Score leads based on company size, industry, and engagement.

3. Route high scores to sales reps, medium scores to demos, low scores to nurturing.

AI thrives on structured flows instead of vague instructions.

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E — Examples

Provide scenarios and edge cases to guide consistent outputs.

• If score ≥ 80 → Assign to senior sales rep + Slack notification.

• If 50–79 → Auto-schedule a demo.

• If < 50 → Send to 6-week nurturing email campaign.

🔑 Tip: Don’t just say “qualify leads”. Show AI the rules of the game.

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A — Adaptation

Iterate with the AI instead of expecting perfection in one shot.

• Start: Draft prompt for the workflow.

• Refine: If output misses something, feed corrections back in.

• Evolve: Adjust until the solution matches real-world constraints.

Think of prompting as conversation + testing, not a one-off command.

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R — Results

Validate the solution against business goals & ROI.

Ask:

• Does the workflow integrate into existing tools (e.g., HubSpot)?

• Can we measure conversions or cost savings?

• Does it solve a $50k+ problem (Nick’s benchmark for “valuable problems”)?

🔑 Tip: Always tie AI work to business outcomes (more revenue, more clients, lower cost).

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🔍 Two Prompts Compared

❌ Without CLEAR
“Build me a lead generation system.”

👉 Output = generic, inconsistent, AI picks random tools.

✅ With CLEAR
“Create an automated lead qualification system for a B2B manufacturing consultancy.

• Inputs: LinkedIn ads + cold email.

• Scoring rules: 50+ employees = 30 pts, Manufacturing industry = 25 pts, Downloaded whitepaper = 20 pts, Booked demo = 40 pts.

• Routing: ≥80 → senior sales rep (Slack notification), 50–79 → auto demo scheduling, <50 → nurturing sequence.

• Integrate with HubSpot and track conversion rates.”

👉 Output = structured, reliable, ready to deploy.

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🧩 Why CLEAR Matters

• Clarity prevents vagueness.

• Logic structures workflows.

• Examples enforce consistency.

• Adaptation makes results practical.

• Results ensure business value.

In 2026+, those who master CLEAR will be the “translators” between business needs and AI capabilities—the real high-value skill.

Let’s take an example. Let’s say that we need to automate things for a mid-sized company.

What would CLEAR look like there?

C — Clarity

Define precise, measurable automation goals.

• Example: “Reduce invoice processing time from 7 days to 2 days”

• Example: “Cut employee onboarding steps from 10 forms to 5 digital workflows.”

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L — Logic

Break processes into sequential steps with clear decision points.

• Example: Expense approvals → Department Head → Finance → Automatic payment.

• Example: Customer support requests → Categorize by urgency → Route to Tier 1 or Tier 2 support.

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E — Examples

Provide edge cases to make automation robust.

• If invoice > $10,000 → Require CFO approval.

• If lead is from manufacturing sector + company size > 100 → Route to enterprise sales team.

• If employee doesn’t upload ID during onboarding → Trigger automated reminder email.

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A — Adaptation

Iterate with AI + staff feedback until workflows stabilize.

• Test automation with small pilot group.

• Adjust triggers (e.g., Slack notifications vs. email) if teams miss alerts.

• Refine workflows monthly as new software or policies emerge.

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R — Results

Tie automation to ROI & business outcomes.

• Track: Hours saved per week, % faster onboarding, error reduction, cost savings.

• Example: Automated invoice approval saves 200 hours/month = $15,000/month in labor.

• Example: Onboarding automation increases employee readiness in first week by 30%.

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For example, a Prompt could be:

Using the CLEAR framework (Clarity, Logic, Examples, Adaptation, Results), create a business automation strategy for a mid-sized B2B services company (~200 employees). The strategy should define measurable goals, break down workflows step-by-step, include real use cases with edge cases, suggest adaptation methods as the company grows, and deliver measurable results tied to ROI. Present it as a structured business plan.

This generates a very usable, point by point strategy that you can follow up, with metrics and KPIs aplenty.

What do you think of CLEAR?