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Prompt Engineering
定義
Prompt engineering is the practice of designing and refining the instructions given to a language model to get accurate, relevant, and reliable outputs for a specific task.
How it works
Because an LLM's output depends heavily on how a request is phrased, prompt engineering involves crafting clear instructions, providing examples, setting constraints, and structuring context so the model behaves predictably. Techniques include giving explicit roles, adding few-shot examples, and specifying the desired format.
In AI support tools, much of this is captured in a system prompt plus the retrieved context, and increasingly in plain-English instructions the platform translates into reliable behavior.
Why it matters for support
The same model can be helpful and accurate or vague and off-brand depending on its prompt. Good prompting is how teams control an AI agent's tone, scope, escalation rules, and adherence to policy — without retraining the underlying model.
よくある質問
Is prompt engineering still necessary with modern LLMs?
Yes, though newer models are more forgiving. Clear instructions, examples, and constraints still meaningfully improve reliability, especially for consistent, on-brand support responses.
What is the difference between prompt engineering and fine-tuning?
Prompt engineering shapes behavior through instructions at query time without changing the model; fine-tuning updates the model's weights by training on examples. Prompting is faster and cheaper to iterate on.
関連用語
System Prompt
A system prompt is the underlying set of instructions that defines an AI model's role, behavior, tone, and boundaries for a conversation — set by the developer, not the end user, and applied to every interaction..
Few-Shot Learning
Few-shot learning is when you guide an AI model to perform a task by including a small number of examples in the prompt, so it can follow the demonstrated pattern without any retraining..
Zero-Shot Learning
Zero-shot learning is when an AI model performs a task it was never explicitly trained or given examples for, relying instead on the general knowledge it learned during pre-training and a clear description of the task..
Fine-Tuning
Fine-tuning is the process of further training a pre-trained language model on a smaller, task-specific dataset so it adapts to a particular domain, style, or behavior — updating the model's weights rather than just its instructions..
Plain-English Agent Configuration
In Macha, you configure an agent by writing its rules in plain English in its instructions field — how to triage, what tone to use, what to escalate, what's off-limits — rather than coding logic.
アイデアを実装に
Macha は既に運用中のヘルプデスク (Zendesk、Freshdesk、Front、Intercom、Gorgias) の上に載る AI エージェント層です。
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