Interactive Planning vs Direct Generation: Which AI Coding Workflow Actually Works?

Apr 1, 2026

The Problem

I kept getting wrong code from my AI coding assistant.

Not subtly wrong. Obviously wrong. It would build features I didn’t ask for. Make architectural decisions I didn’t want. Create files I didn’t need.

Then I’d spend more time fixing the AI’s assumptions than if I had just written the code myself.

A Reddit thread confirmed I wasn’t alone. Someone reported 90% success rate after switching to interactive planning. I had to understand why.

Two Workflows, Two Outcomes

Here’s what my direct generation workflow looked like:

Me: "Add authentication to my app"
AI: [generates full implementation with JWT, password reset, email verification]
Me: "I wanted session-based auth..."
AI: [revises]
Me: "And I didn't ask for password reset or email verification"
AI: [revises again]
Me: "Now the tests are failing because you changed the database schema"
AI: [fixes tests]
... and so on

The interactive planning workflow is different:

Me: "Use grill-me to add authentication"
AI: "What type of auth do you need? I see your app is single-server,
    so session-based would be simpler. JWT is better for distributed systems."
Me: "Session-based"
AI: "What persistence layer? I see Redis in your dependencies."
Me: "Redis"
AI: "Should I include password reset? For MVP, maybe skip it?"
Me: "Skip it, MVP only"
AI: "Got it. I'll implement session-based auth with Redis, no password reset.
    Generating now..."
AI: [generates aligned implementation]

The second approach works better for complex work. But why?

The Core Difference

AI coding tools have a default behavior: generate immediately.

This is great for speed. Bad for accuracy when context matters.

┌─────────────────────────────────────────────────────────────────┐
│                    Direct Generation                            │
├─────────────────────────────────────────────────────────────────┤
│  User Request ──► AI Assumptions ──► Generated Code             │
│                         │                                        │
│                         ▼                                        │
│                    [Unknown Unknowns]                            │
│                    AI fills gaps with guesses                    │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│                   Interactive Planning                          │
├─────────────────────────────────────────────────────────────────┤
│  User Request ──► Q&A One at a Time ──► Shared Understanding   │
│                         │                    │                  │
│                         ▼                    ▼                  │
│                    AI explores           [Known Knowns]        │
│                    codebase              No guessing needed    │
└─────────────────────────────────────────────────────────────────┘

The Reddit post documented this clearly. With direct generation, the user faced constant revision cycles. With interactive planning: 90% accuracy on first attempt.

When Each Works

I analyzed my own usage patterns and found a clear split:

Factor	Direct Generation	Interactive Planning
Initial speed	Seconds	Minutes (Q&A time)
Assumption risk	High	Zero (asks instead)
Over-engineering	Common	Rare (confirmed scope)
Revision cycles	0-5+	0-1 usually
Best for	Simple, defined tasks	Complex, ambiguous work

The decision framework looks like this:

                    ┌────────────────────────────┐
                    │ What's your task complexity │
                    └────────────────────────────┘
                              │
               ┌──────────────┼──────────────┐
               │              │              │
               ▼              ▼              ▼
           Simple         Moderate       Complex
        (1-2 lines     (file/module)  (architecture)
         or obvious)                    │
               │              │         │
               ▼              ▼         ▼
        ┌──────────┐   ┌──────────┐   ┌──────────┐
        │  Direct  │   │ Hybrid   │   │Interactive│
        │Generation│   │(1-2 Qs) │   │ Planning │
        └──────────┘   └──────────┘   └──────────┘

Direct Generation: When It Works

For simple tasks, direct generation is the right tool:

"Fix the null pointer exception in UserService.java at line 45"

"Add logging to the payment processing function"

"Refactor this loop to use streams"

These work because:

Single file, clear location
Specific action requested
No architectural decisions

The AI doesn’t need to guess. I gave it everything.

Interactive Planning: When You Need It

The warning signs that direct generation will fail:

The AI output has:
- Features you didn't request
- Architecture you didn't want
- Multiple files you didn't expect
- "Helpful" additions that aren't helpful

When I see these, I switch to interactive planning.

How to invoke it depends on the tool. For tools with skills:

"Use grill-me to help me design the caching strategy"

"$grill-me: Add a user notification system"

For tools without skills, plain English works:

"Brainstorm with me about this feature"

"Let's discuss the design first. Ask me questions one at a time."

"Before coding, help me think through the edge cases"

The principle is the same: interrupt the “generate immediately” default.

Why Interactive Planning Works

The Reddit discussion revealed something interesting. Multiple people reported success with different implementations of the same idea:

Approach	Description
grill-me skill	Structured Q&A interview
”brainstorm with me”	Casual prompt variation
ChatGPT pre-refinement	Refine elsewhere, paste prompt
GSD skill	Full project breakdown
Interactive multiple choice	Claude Code’s arrow-key selection

They all share one thing: questions before generation.

This works because:

One question at a time - Not overwhelming, forces focus
AI provides reasoning - “I recommend X because your setup suggests Y”
AI explores codebase - Reduces my explanation burden
Shared understanding reached - Before any code is written

The result: the AI has all the context it needs, and I’ve confirmed every decision.

The Hybrid Approach

For moderate complexity, I use a hybrid:

"Add search functionality. First, ask me 2-3 clarifying questions about
the search requirements, then implement."

This gives me the benefit of Q&A without a full interview. The AI asks what matters most, then proceeds.

The Takeaway

I used to think AI coding tools were either good or bad. The reality: they’re good at different things.

Direct generation excels at:

Bug fixes in specific locations
Simple refactoring
Adding straightforward features

Interactive planning excels at:

Architectural decisions
Features with multiple approaches
Anything where “helpful” additions would hurt

The mistake I made was using direct generation for everything. Once I started matching the approach to the task, my success rate improved dramatically.

The “brainstorm with me” prompt variant shows the principle isn’t skill-specific. Any approach that forces Q&A before generation will improve outcomes for complex work.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit: I tried the grill-me skill and it completely changed my workflow
👨‍💻 grill-me skill on Skills.sh

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!