Lecture IV — Planning with AI

AI-Assisted Programming for PhD Researchers

Dr. Tobias Vlćek

Helmut Schmidt University — September 2026

Why Plan First

Vibe Coding

  • Prompt, run, prompt again, accepting the output without reading it
  • Fine for a throwaway script; ruinous for research code
  • The first empirical study of the pattern found “impressionistic scanning”, not review (Sarkar and Drosos 2025)
  • For code you will publish or build on, this is the anti-pattern

How Unplanned Agent Work Fails

  • Scope drift: the change quietly grows past what you asked for
  • Architecture by accident: structure no one chose, no one owns
  • Unreviewable diffs: 800 lines you cannot meaningfully check
  • “It broke something elsewhere”: no plan meant no boundaries

The One-Sentence Rule

A quick test for whether you need a plan at all:

If you can describe the change in one clear sentence, just do it. Everything bigger gets a plan first.

Specifications

What a Spec Is

  • A short document you write before any code
  • It fixes four things: goal, inputs/outputs, constraints, acceptance criteria
  • Not a design essay — half a page is plenty
  • It is what you and the agent both agree to

Acceptance Criteria Do the Work

  • Each criterion must be checkable — pass or fail, no debate
  • Vague in, vague out: “handles missing values” tells the agent nothing
  • Sharpen it: rows with humidity n/a, empty, or -99 become NaN
  • Now both of you know when the code is done

Example: Our Cleaning Module

The Lab 2 spec skeleton — you complete it in the lab:

# Spec: data loading and cleaning
Module: src/pipeline/io.py — load_raw(path), clean(df).
Acceptance criteria:
  [ ] three timestamp formats -> one datetime column
  [ ] temps unified to °C (temp_unit consulted, then dropped)
  [ ] humidity "", "n/a", -99 -> NaN; decimal commas parsed
  [ ] wind "calm" -> 0.0; column numeric
  [ ] station names stripped of whitespace
  [ ] exact duplicate rows dropped; report how many
Out of scope: statistics, plotting, the CLI.

Specs Anchor the Conversation

  • Agents drift — they forget, reinterpret, wander off task
  • The spec is the fixed point you keep pointing back to
  • Disagreements resolve against the spec, not against memory
  • “That is not in the spec” ends most arguments fast

The Interview Pattern

Make the Agent Interview You

Paste this before you describe the task:

Before writing any code, ask me clarifying
questions about requirements and edge cases,
one at a time, until the task is unambiguous.
Only then propose a plan.

Surprisingly effective, and it costs you one extra prompt.

Why It Works

  • You know things you do not know you know — tacit constraints
  • Questions surface hidden requirements early
  • Early is when they are cheap; after 800 lines, they are not
  • The agent’s questions are often sharper than your first brief

Decomposition

Verifiable Steps

  • Break the work into steps that each end in something you can check
  • A passing run, an inspectable output, a green test
  • No step longer than ~30 minutes of agent work
  • If you cannot name the check, the step is not ready

Plan Mode Workflow

  • In plan mode the agent explores and drafts a plan, editing nothing
  • You review it, adjust, and only then switch to build mode
  • The plan is a contract for the session
  • This “generate, then evaluate” loop is what the GenAI education literature recommends (Bouvier et al. 2025)

Reviewing a Plan

Before you approve, ask three questions:

Does it cover every acceptance criterion? Does it invent scope you never asked for? Does each step end in a check?

Lab 2

Your Mission

In Lab 2 you put planning to work on the cleaning module:

  • Write the cleaning spec — goal, I/O, constraints, acceptance criteria
  • Let the agent plan against it in plan mode, then build step by step
  • Verify on real station data — not on the agent’s say-so

Lab 2 — Spec to Code

Continue Your Journey

Next Up

Bouvier, Dennis J., Bruno Pereira Cipriano, Richard Glassey, et al. 2025. “The Rest of the Robots: Generative AI in Post-Introductory Computing Education.” Proceedings of the 2025 Working Group Reports on Innovation and Technology in Computer Science Education (Nijmegen Netherlands), June, 61–107. https://doi.org/10.1145/3760545.3783970.
Sarkar, Advait, and Ian Drosos. 2025. “Vibe Coding: Programming Through Conversation with Artificial Intelligence.” Proceedings of the 36th Annual Conference of the Psychology of Programming Interest Group (PPIG 2025), September. https://doi.org/10.48550/ARXIV.2506.23253.