Part III: Vibe-Coding and AI-Native Prototyping
Chapter 12

Prompting, Context, Memory, and Reusable Skills

You have probably experienced this: one AI session produces elegant, working code; the next session, with a nearly identical prompt, produces gibberish. The difference is not the model. It is not even the prompt. It is context management: how you structure information, how you establish conventions, how you preserve memory across sessions. Prompting is not about magic incantations. It is about engineering the context environment that lets AI consistently produce valuable outputs.
The Tripartite Loop in Prompt Engineering

Engineering prompts for production requires all three disciplines: AI PM defines what the AI should accomplish, what style and tone are appropriate, and what constraints apply; Vibe-Coding experiments with different prompt structures, context arrangements, and memory patterns to find what produces the best outputs; AI Engineering implements prompt versioning, testing infrastructure, and monitoring to track prompt performance over time and across model updates.

Chapter 12 opener illustration
Prompts are the interface to AI systems, requiring as much care as APIs and UX.

Objective: Master advanced prompting techniques, context management, and reusable skills.

Chapter Overview

This chapter covers durable abstractions over tools, the building blocks of prompts, rules, skills, templates, and memory, and how to establish repository conventions for teams. For deeper coverage of memory patterns in production systems, see Chapter 18.

Four Questions This Chapter Answers

  1. What are we trying to learn? How to create reusable, team-friendly AI interaction patterns that survive tool churn and scale across projects.
  2. What is the fastest prototype that could teach it? Building a small skill library for one recurring task and measuring how much it reduces per-use setup time.
  3. What would count as success or failure? Reusable skills that new team members can apply without deep context, versus one-off prompts that live only in individual sessions.
  4. What engineering consequence follows from the result? Investing in durable abstractions (skills, templates, conventions) prevents the common failure of AI knowledge being trapped in individual sessions.

Learning Objectives

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