Frameworks for AI-Augmented Work: Decision Trees and Quality Gates

Meta Description: 90 days, 132 tasks, 160+ hours saved. Here are the frameworks: decision trees, prompt patterns, quality gates, and workflows that turn AI experiments into systems.

Everyone’s experimenting with AI. Few are systematizing it.

Experiments are one-offs. Systems compound.

We ran 132 AI-assisted tasks over 90 days. Saved 160+ hours. But the real value isn’t the time saved—it’s the frameworks we built that make AI productivity repeatable.

Here are the decision trees, prompt patterns, quality gates, and workflows that turn AI from an experiment into a system.

The Meta-Framework: Experiments → Patterns → Systems

Most teams get stuck in the experiment phase:

Experiment phase:

• Try AI for random tasks
• Inconsistent results
• Can’t predict when it’ll work
• Time savings are random

Pattern phase:

• Notice what works and what doesn’t
• Document successful approaches
• Build checklists and templates
• Time savings become predictable

System phase:

• Encode patterns into reusable workflows (skills)
• AI handles repetitive work consistently
• Humans focus on strategy and review
• Time savings compound

Where most teams fail: They never move from experiments to patterns. They keep treating AI like a magic box instead of a tool with known strengths and limitations.

Our 90-day journey:

• Days 1-30: Experiments (trying everything, inconsistent results)
• Days 30-60: Patterns (documenting what works, building checklists)
• Days 60-90: Systems (skills, workflows, repeatable processes)

Result: 160+ hours saved, 95%+ success rate on AI-assisted tasks

Framework 1: The AI Decision Tree

When should you use AI? When should you go manual? Here’s the decision tree we use:

Is the task well-defined with clear success criteria?
├─ NO → Do it manually OR use AI to explore/clarify first
└─ YES → Continue

Does the task involve novel creativity or strategic judgment?
├─ YES → Do it manually (AI assists, human leads)
└─ NO → Continue

Does the task require context from 10+ files or sources?
├─ YES → Use AI (humans forget context, AI doesn't)
└─ NO → Continue

Is the task repetitive with consistent quality criteria?
├─ YES → Build a skill/workflow (systematize it)
└─ NO → Continue

Would manual completion take < 5 minutes?
├─ YES → Just do it manually (setup time > savings)
└─ NO → Use AI

Is this the first time doing this type of task?
├─ YES → Use AI with heavy review (learn what works)
└─ NO → Use AI with light review (pattern is proven)

Does the task involve sensitive data or high-risk decisions?
├─ YES → AI assists, human makes final decision
└─ NO → AI can handle with review

Real Examples:

Task: Refactor authentication across 8 files

• Well-defined? ✅ (clear pattern to apply)
• Novel creativity? ❌ (applying known pattern)
• Context from 10+ files? ✅ (8 files + dependencies)
• Repetitive? ✅ (similar refactoring pattern)
• Takes > 5 min? ✅ (3 hours manual)
• Decision: Use AI with skill/workflow

Task: Design new product pricing model

• Well-defined? ❌ (strategy decision, unclear criteria)
• Novel creativity? ✅ (strategic judgment required)
• Decision: Do manually (AI can research, human decides)

Task: Fix typo in README

• Takes > 5 min? ❌ (30 seconds manual)
• Decision: Just fix it manually

Task: Generate 1,200 jailbreak tests

• Well-defined? ✅ (test attack categories)
• Repetitive? ✅ (same pattern, many variations)
• Context from 10+ files? ✅ (needs to understand 10 prompts)
• Decision: Use AI, build it into workflow

Framework 2: Prompt Engineering Patterns

After 132 tasks, these prompt patterns work consistently:

Pattern 1: Context-First Prompting

Bad (vague):

Refactor this code to be better.

Good (context-first):

I'm refactoring authentication middleware to separate concerns.

Current state: Session management is mixed with route handlers (8 files)
Desired state: Middleware handles all session logic, routes stay focused

Please:
1. Read all 8 route files
2. Extract session management into middleware
3. Update routes to use the middleware
4. Maintain exact same behavior (this is refactoring, not rewriting)

Files to read: [list]

Why it works: AI knows what success looks like, has full context, and has clear constraints.

Pattern 2: Constraints Over Instructions

Bad (instructions only):

Write a function that validates email addresses.

Good (constraints):

Write a function that validates email addresses.

Constraints:
- Must handle edge cases (unicode, plus-addressing, subdomains)
- Must return specific error messages (not just true/false)
- Must be testable (pure function, no side effects)
- Must not use regex (too brittle for email validation)

Return format:
{valid: boolean, error: string | null}

Why it works: Constraints prevent common AI mistakes. “Don’t use regex” stops AI from suggesting regex hell.

Pattern 3: Example-Driven Prompting

Bad (abstract):

Use our brand voice for this blog post.

Good (examples):

Write this blog post using Alien Brain Trust voice.

Our voice (examples):
- "Security testing is tedious. AI doesn't zone out."
- "We saved 52 hours in 90 days. Here's how."
- "Everyone's asking the wrong question about AI."

Characteristics:
- Short sentences for emphasis
- Lead with data
- No buzzwords ("revolutionary," "game-changing")
- Honest about failures

Now write the post.

Why it works: AI learns from examples better than from abstract rules.

Pattern 4: Checklist-Based Prompting

Bad (open-ended):

Review this code for issues.

Good (checklist):

Review this code against this security checklist:

- [ ] No hardcoded secrets or API keys
- [ ] SQL queries use parameterized statements
- [ ] User input is validated/sanitized
- [ ] Auth/authorization is correct
- [ ] Error messages don't leak sensitive info

For each item, either check it off or explain the issue + suggest a fix.

Why it works: Checklists ensure comprehensive coverage. AI doesn’t forget items like humans do.

Pattern 5: Iterative Refinement

Bad (one-shot):

Build a complete authentication system.

Good (iterative):

Step 1: Read the existing auth code and explain the current architecture.
[Review AI's understanding]

Step 2: Identify what needs to change for the new requirements.
[Review AI's analysis]

Step 3: Propose a migration plan (not implementation yet).
[Approve or adjust plan]

Step 4: Implement the first component (middleware).
[Review, test, iterate]

Step 5: Implement remaining components one at a time.

Why it works: Complex tasks need human checkpoints. Iterative approach catches errors early.

Framework 3: Quality Gates

AI output needs review. Here are the quality gates that catch 95% of issues:

Gate 1: Accuracy Check (Does It Work?)

For code:

• Code compiles/runs without errors
• Tests pass
• No new security vulnerabilities introduced
• Behavior matches requirements

For content:

• Facts are accurate (no hallucinations)
• Examples are realistic (not foo, bar, baz)
• Data is correct (verify numbers)
• Links work

Time: 5-10 minutes per task

Gate 2: Completeness Check (Did It Do Everything?)

• All requirements addressed
• No TODOs or placeholders left
• Edge cases handled
• Error handling included
• Documentation updated

Time: 2-5 minutes per task

Gate 3: Quality Check (Is It Good?)

For code:

• Follows codebase patterns and style
• No over-engineering (simple solution, not clever one)
• Performance is acceptable
• Code is readable and maintainable

For content:

• Brand voice is correct
• Tone is appropriate for audience
• Structure makes sense
• No buzzwords or generic fluff

Time: 5-10 minutes per task

Gate 4: Security Check (Is It Safe?)

• No secrets or credentials in output
• Input validation is correct
• Auth/authorization is handled
• No SQL injection, XSS, or common vulnerabilities
• PII is handled appropriately

Time: 5-10 minutes for security-sensitive tasks

Total review time: 15-30 minutes per task Compare to: 60-180 minutes to do task manually Net savings: Still 50-80% time reduction even with thorough review

Framework 4: The Skills System (Repeatable Workflows)

When you do the same type of task 3+ times, build a skill:

Anatomy of a Good Skill

# Skill Name: /skill-name

## Purpose
[One sentence: what this skill does]

## When to Use
[Clear criteria for when to invoke this skill]

## Process
1. [Step 1 - specific action]
2. [Step 2 - specific action]
3. [Step 3 - specific action]

## Quality Checklist
- [ ] Criterion 1
- [ ] Criterion 2

## Output Format
[Exactly what format the output should be]

## Examples
[2-3 real examples of good output]

## Safety Rails
NEVER:
- [Thing that would be dangerous]

ALWAYS:
- [Thing that must be done]

Skills We Use Daily (from earlier posts):

1. /commit - Git commits with standards
2. /review-pr - Pull request review with security checklist
3. /fix-tests - Test script validation
4. /blog-post - Write blog posts in brand voice
5. /refactor - Code refactoring with safety checks
6. /security-scan - Vulnerability scanning
7. /document - Generate documentation

Time saved from skills: 43+ hours in 90 days

Key insight: Skills encode your standards. AI applies them consistently. Humans just review.

Framework 5: The Learning Loop

How to get better at AI-augmented work:

Week 1-2: Track Everything

• Log every AI-assisted task
• Note: task type, time saved, quality, what worked, what didn’t
• Build a spreadsheet: [Task, Tool, Time (AI), Time (Manual), Quality, Notes]

Week 3-4: Find Patterns

• What tasks show consistent time savings?
• What tasks show consistent quality issues?
• What prompts work repeatedly?
• What types of tasks should you avoid with AI?

Week 5-6: Build Checklists

• Create quality checklists for common tasks
• Document prompt patterns that work
• Write down AI strengths and limitations for your use cases

Week 7-8: Create Skills

• Pick the top 3 repetitive tasks
• Build skills/workflows for them
• Test on 5 real examples each
• Refine based on results

Week 9+: Systematize

• Make skills the default for repetitive work
• Train team on when/how to use them
• Continuously improve based on feedback
• Track ROI (time saved, quality, cost)

Timeline: Most teams reach systematic AI usage in 60-90 days

Framework 6: Team Adoption

How to roll out AI-augmented work to your team:

Phase 1: Prove Value (Weeks 1-2)

• You use AI for 2 weeks, track results
• Share specific wins: “AI saved 4 hours on this refactor”
• Focus on time saved and quality improved
• Don’t force adoption yet

Phase 2: Enable Early Adopters (Weeks 3-4)

• Share tools and frameworks with interested team members
• Provide training: decision trees, prompt patterns, quality gates
• Help them with their first 5 tasks
• Gather feedback and iterate

Phase 3: Build Team Patterns (Weeks 5-8)

• Document what works for your team specifically
• Create team-specific skills for common tasks
• Establish quality standards
• Share success stories internally

Phase 4: Make It Default (Weeks 9+)

• Use AI-augmented workflows by default for repetitive tasks
• Human review is mandatory (quality gates)
• Track team-wide metrics (time saved, quality, ROI)
• Continuously improve based on team feedback

Key principle: Show, don’t tell. Demonstrate value before pushing adoption.

Real-World Application: Our Daily Workflow

Here’s what AI-augmented work looks like in practice:

Morning (8:00 AM - 8:30 AM)

# Review overnight activity
/review-pr [latest PR]           # AI reviews code, I check findings
/linear triage [support emails]  # AI categorizes support, I approve

# Plan the day
Claude reads GitHub issues, I prioritize top 3

Development (9:00 AM - 12:00 PM)

# Feature work
1. I design architecture (human judgment)
2. Claude implements across multiple files (AI speed)
3. I review changes (human quality check)
4. Copilot handles boilerplate (AI autocomplete)
5. /fix-tests on test suite (AI consistency)

Documentation (12:00 PM - 12:30 PM)

# Document new feature
/document [feature code]         # AI generates docs
I review and add "why" context   # Human adds purpose
Grammarly final pass            # AI editing

Afternoon (1:00 PM - 5:00 PM)

# Security testing
/security-scan [new code]        # AI checks for vulnerabilities
I review and fix issues          # Human prioritizes and fixes

# Code review
/review-pr [team member's PR]    # AI does first pass
I review business logic          # Human checks strategy

End of day (5:00 PM - 5:30 PM)

# Commit work
/commit                          # AI drafts commit message
I review and adjust              # Human ensures accuracy

# Update tracking
/linear status-update [project]  # AI reads commits, drafts update
I add strategic context          # Human adds next steps

Total AI usage: 2-3 hours/day Total time saved: 3-4 hours/day (net: +1-2 hours productivity) Human focus: Architecture, strategy, quality, judgment

The Bottom Line

160+ hours saved in 90 days across 132 tasks.

But time saved isn’t the real story.

The real story: We turned AI experiments into systems.

Experiments:

• Random tasks
• Inconsistent results
• Can’t predict success
• Time savings don’t compound

Systems:

• Clear decision trees (when to use AI)
• Proven prompt patterns (how to use AI)
• Quality gates (ensuring output is good)
• Reusable skills (encoding standards)
• Learning loops (continuous improvement)

The frameworks in this series:

1. Decision tree - When to use AI vs. manual
2. Prompt patterns - Context-first, constraints, examples, checklists, iteration
3. Quality gates - Accuracy, completeness, quality, security
4. Skills system - Reusable workflows with built-in guardrails
5. Learning loop - Track → patterns → checklists → skills → systematize
6. Team adoption - Prove value → early adopters → team patterns → default

The question isn’t “How much time can AI save?”

It’s: “How do we turn AI experiments into repeatable systems?”

That’s where the real leverage is.

Not one-off wins. Compounding productivity.

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Series Recap

Over 9 posts, we covered:

1. Series Intro - 160+ hours saved across 132 tasks in 90 days
2. Code Development - What companies look like with AI (2-person teams doing 5-person work)
3. Security Testing - 52 hours saved, 73 vulnerabilities fixed, 1,200+ tests generated
4. AI as Intern - Skills as guardrails, 43+ hours saved from 7 daily skills
5. Documentation - 60% time reduction (22 hours saved) with template-driven approach
6. Content Creation - 3-pass editing system maintains brand voice (14 hours saved)
7. Project Management - 8 hours saved on busywork through automation
8. Tools Comparison - Claude vs. ChatGPT vs. specialized tools, $62/month optimal stack
9. Frameworks (this post) - Decision trees, prompt patterns, quality gates, systems

Total documented savings: 160+ hours Total cost: $62/month ROI: 4,500%+ (conservative)

The real value: Not time saved. Systematic leverage.

Thanks for following this series. Now go build your own AI-augmented workflows.

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