Overview

AI Problem Framing is Layer 2 of AI fluency—the ability to transform fuzzy intent into structured problems that AI can solve well. This is the highest-leverage skill because AI amplifies your framing, not your intent.

Core Principle: AI doesn't solve problems—it amplifies the framing you give it.

Fluency Signal: Get high-quality output on the first iteration.

When to Use This Skill

Before any significant AI delegation
When AI output keeps missing the mark
When iteration feels random rather than directional
When defining AI-assisted workflows
When training others on effective AI use

The Framing Framework

Every AI task requires three elements:

1. Explicit Objectives

What you actually want, not what you're asking for.

Weak (Vague Intent)	Strong (Explicit Objective)
"Analyze this data"	"Identify the 3 metrics most correlated with customer churn"
"Write about X"	"Draft a 500-word explanation of X for non-technical readers"
"Help me think about Y"	"Generate 5 distinct strategic options for Y with trade-offs"
"Improve this code"	"Reduce the time complexity of this function from O(n²) to O(n log n)"

Test: Would two different AIs produce similar outputs from this objective?

2. Constraints

Boundaries that shape the solution space.

Types of constraints:

Format: Length, structure, output type
Scope: What's in/out of bounds
Style: Tone, voice, technical level
Resources: What information to use (or not use)
Quality: Standards that must be met

Example:

Objective: Write a product description
Constraints:
- Maximum 150 words
- Include price and 3 key features
- Match brand voice (casual, confident)
- Do not mention competitors
- Must include call-to-action

3. Success Criteria

How you'll know the output is good.

Success criteria should be:

Specific: Measurable or evaluable
Complete: Cover all important dimensions
Prioritized: Which matter most if trade-offs needed

Example:

Success criteria (in priority order):
1. Factually accurate (all claims verifiable)
2. Addresses all 3 user questions
3. Under 500 words
4. Professional tone
5. Includes recommended next steps

Problem Decomposition

Breaking Work into AI-Appropriate Chunks

Large problems require decomposition:

Identify sub-tasks - What distinct pieces of work exist?
Sequence them - What depends on what?
Assign ownership - Human, AI, or hybrid?
Define interfaces - What moves between steps?

Decomposition Pattern

[Original Problem]
    ↓
[Sub-task 1: Research] → AI (good at synthesis)
    ↓
[Sub-task 2: Analysis] → Human (requires judgment)
    ↓
[Sub-task 3: Drafting] → AI (good at generation)
    ↓
[Sub-task 4: Review] → Human (requires accountability)
    ↓
[Sub-task 5: Refinement] → AI + Human (iteration)

Artifact Mapping

For each sub-task, define:

Input artifact: What goes in
Output artifact: What comes out
Verification method: How to check quality

Human vs AI Responsibility

What Must Stay Human

Final decisions: AI advises, humans decide
Accountability: You own the output
Value judgments: Ethics, priorities, trade-offs
Verification: Checking against reality
Context integration: Understanding full situation

What AI Does Well

Pattern synthesis: Combining information
Variation generation: Multiple options
Format transformation: Restructuring content
Search augmentation: Finding relevant information
Draft creation: First-pass content

The Boundary Decision

For each task element, ask:

Can AI do this reliably? (Capability)
Can I verify the output? (Verifiability)
Do I understand it enough to catch errors? (Expertise)
What's the cost of AI error? (Risk)

If any answer is unfavorable, keep it human.

Practices

Problem Statement Template

Before delegating, complete:

## Problem Statement

**Objective:** [What I actually want to achieve]

**Context:** [Background AI needs to understand]

**Constraints:**
- [Constraint 1]
- [Constraint 2]
- [Constraint 3]

**Success Criteria:**
1. [Most important criterion]
2. [Second most important]
3. [Third most important]

**Out of Scope:** [What I don't want]

**Artifacts:**
- Input: [What I'm providing]
- Output: [What I expect back]

**Verification:** [How I'll check quality]

Task → Artifact Mapping

For complex work:

Task	Input	Output	Owner	Verification
Research competitors	Industry list	Competitor profiles	AI	Spot-check 2-3
Identify gaps	Profiles + our features	Gap analysis	Human	N/A (judgment)
Draft positioning	Gap analysis	Positioning options	AI	Review all options
Select positioning	Options	Decision + rationale	Human	N/A (decision)

Constraint-First Framing

Instead of starting with what you want, start with what you don't want:

List everything that would make output unacceptable
Convert to positive constraints
Prioritize constraints
Then add objectives

This prevents scope creep and ensures critical requirements aren't missed.

Assessment Criteria

Layer 2 Complete When:

Can write task specs that multiple AIs would handle consistently
Distinguishes objectives from activities
Specifies measurable success criteria before delegation
Correctly assigns tasks to human vs AI ownership
Has reduced iteration cycles through better framing

Common Framing Failures

Failure 1: Activity vs Outcome

Wrong: "Analyze customer feedback" Right: "Identify the top 3 complaints and their frequency in customer feedback"

Failure 2: Implicit Constraints

Wrong: "Write a blog post about AI" Right: "Write a 800-word blog post about AI for marketing professionals, avoiding jargon, in our brand voice (examples attached)"

Failure 3: Missing Success Criteria

Wrong: "Make this better" Right: "Improve clarity for non-technical readers; success = a 10th grader can understand the main point"

Failure 4: Unbounded Scope

Wrong: "Help me plan my project" Right: "Create a 10-item task list for the research phase of the project, with dependencies and estimated durations"

Related Skills

ai-instruction-design — Translating framing into prompts
ai-cognitive-readiness — Knowing when to frame vs not delegate
ai-fluency-antipatterns — Vague Intent Delegation anti-pattern

ai-problem-framing