Data Archive

Code Prompting with AI

Core Position

AI-assisted programming functions as a capability multiplier. Output quality is primarily determined by the user’s technical understanding, problem framing, and communication precision.

1. Preconditions for Effective Use

  • Competent programming knowledge is required.
  • AI can automate typing and scaffolding; it does not replace problem solving or architectural reasoning.
  • Delegating thinking degrades outcomes and user skill relevance.

2. Prompt Specificity as the Main Control Lever

  • AI performance scales with contextual and technical detail.
  • Vague prompts force architectural guesses, increasing brittle or misleading code.
  • High-quality prompts include:
    • Explicit tech stack
    • Architectural constraints
    • Commands, workflows, and runtime expectations
    • References to documentation and examples
    • Visual or structural cues where applicable

3. Prompt Maturity Levels (Observed Pattern)

  • Minimal: Insufficient context; either refusal or low-quality output.
  • Descriptive but non-technical: Partial scaffolding, errors, missing decisions.
  • Fully technical: Runnable code, aligned architecture, fewer defects.

Key insight: variance in results is more attributable to prompt quality than model quality.

4. Task Decomposition

  • AI performs best on bounded, well-defined tasks.
  • Large problems should be decomposed into smaller units before prompting.
  • Inability to decompose indicates insufficient problem understanding.
  • This mirrors standard engineering practice, independent of AI.

5. Structured Prompt Pattern

Recommended three-part structure:

  1. Task
    • Explicit objective and success criteria.
  2. Context
    • Codebase references, documentation, assets, examples, screenshots.
  3. Constraints (Do Not Section)
    • What must not be changed.
    • What is out of scope.
    • Explicit boundaries on files, APIs, or behaviors.

This structure materially reduces unintended changes and low-signal output.

6. Prompt Refinement via AI

  • Draft a technically complete prompt.
  • Ask AI to rewrite or enhance it using LLM prompting best practices.
  • Use the refined prompt for execution tasks.

7. Persistent Project Context

  • Maintain a project-level rules file (e.g. guidelines.md, agent.md).
  • Contents:
    • Project purpose
    • Tech stack
    • Commands and workflows
    • Architectural constraints
    • Domain-specific rules
  • Can be authored manually, generated by AI, or sourced from templates.
  • Acts as long-term memory for the assistant.

8. Tooling via MCP (Model Context Protocol)

  • MCPs extend AI context with live or structured data.
  • Examples:
    • Documentation retrieval
    • Framework-specific project state
    • Browser developer tools
  • Select MCPs aligned with the stack rather than copying generic setups.

9. Verification as a First-Class Requirement

  • AI-generated code must include a verification path:
    • Tests
    • CLI commands
    • Build steps
    • Runtime checks
  • Verification can be generated by AI but must be validated by the user.
  • Front-end tasks benefit strongly from tooling-backed verification.

10. Habit Amplification Effect

  • AI amplifies existing engineering habits:
    • Good habits → improved leverage and velocity.
    • Poor habits → faster accumulation of technical debt.
  • Documentation, testing, task breakdown, and constraint-setting remain decisive skills.

Practical Summary

  • Think first, prompt second.
  • Be explicit, technical, and constrained.
  • Break problems down before delegating.
  • Preserve project context persistently.
  • Require verification paths.
  • Treat AI as an execution assistant, not a reasoning substitute.

Backlinks