Prompt Engineering
Prompts as the programming interface of AI work: system prompts, instruction strengthening, and the point where a prompt hands off to a schema.
The prompt is where most AI behavior is actually specified — and the AISL material treats it as an engineering surface, not a wish. Across the courses and workshops, prompts get versioned, strengthened, tested, and — when prompts alone stop holding — replaced by schemas.
Instructions as a layer
AI Hero: 7-Day AI Agents Crash-Course day 2 gives the system prompt its own lesson: instructions are a distinct layer of the application, separate from the user's question and the retrieved context. Building AI Agents with MCP, PydanticAI, and OpenAI shows the iteration loop on that layer — Part 3 is literally titled "Make the instructions stronger", done after a plain model call proves too timid.
The prompt-to-schema handoff
The clearest statement of the limits is in the AI Engineering Buildcamp video-summary sequence: prompt engineering extracts summaries with chapters and timestamps, and works — until the timestamps drift and the format wobbles. That is the handoff point to Structured Output: a prompt says what you want; a schema guarantees it.
Prompts for agents and coding agents
Two specialties recur. Coding agents put behavior in the developer prompt — Building a Coding Agent with Python and Django argues it deserves most of your attention, and the skills and slash commands workshops are that idea made reusable. Voice is the other: Building a Voice Coach with ElevenLabs designs the coaching prompt first and only then wires the audio.
Where to go next
- When the format must hold: Structured Output
- Prompts driving tool loops: Agents
- Prompts driving coding agents: Building Coding Agents
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