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It can include role framing, reference context, examples, prohibited behavior, structured output, and acceptance criteria. It does not permanently change the model, so it has limits when the task needs domain adaptation, private data retrieval, or stronger safety controls. Production use should pair prompts with test cases, failure examples, review rules, permissions, and logs.", "items": [] }, { "key": "usage", "title": "役立つ場面", "text": "Teams can decide whether prompt changes are enough or whether RAG, tool use, or fine-tuning is required. Reusable templates reduce dependence on individual writing style. Documented failure patterns become regression tests when models, data, or workflows change.", "items": [ "Teams can decide whether prompt changes are enough or whether RAG, tool use, or fine-tuning is required.", "Reusable templates reduce dependence on individual writing style.", "Documented failure patterns become regression tests when models, data, or workflows change." ] }, { "key": "usage", "title": "使い方のポイント", "text": null, "items": [ "Prompt engineering is input, constraint, and evaluation design, not just clever wording.", "A strong prompt states the goal, context, output format, prohibited behavior, and acceptance criteria.", "Improvement requires iteration against test cases and observed failures.", "Prompts alone do not guarantee factuality, permissions, or security.", "Business use needs templates, logs, and review ownership." ] }, { "key": "drivers", "title": "何が数字を動かすか", "text": "Prompt quality depends more on goal clarity, constraints, and evaluation criteria than on prompt length. Goal | Clear outcomes focus the response Output format | Tables, bullets, or JSON make downstream use easier Examples | Good and bad examples align judgment Evaluation | Acceptance tests make iteration measurable", "items": [ "Goal | Clear outcomes focus the response", "Output format | Tables, bullets, or JSON make downstream use easier", "Examples | Good and bad examples align judgment", "Evaluation | Acceptance tests make iteration measurable" ] } ], "misunderstandings": [ { "key": "misunderstandings", "title": "判断するときの注意点", "text": "Do not use prompts as the only control for truth, privacy, or safety. Verify important facts with trusted sources or systems of record. Longer prompts are not automatically better; irrelevant context can reduce quality. Internal templates should show prohibited data and safe examples, not only ideal prompts.", "items": [ "Verify important facts with trusted sources or systems of record.", "Longer prompts are not automatically better; irrelevant context can reduce quality.", "Internal templates should show prohibited data and safe examples, not only ideal prompts." ] }, { "key": "misunderstandings", "title": "よくある誤解 / 落とし穴", "text": null, "items": [ "There is no universal magic prompt. Production quality depends on tests and operations.", "Long prompts are not automatically good. Clear constraints matter more than volume.", "Prompts alone cannot provide security. Permissions, validation, UI, and logging still matter." ] } ], "examples": [ { "key": "examples", "title": "最小例", "text": "A revenue operations team builds a prompt to extract next actions from sales call notes. The first version returns too many low-value items, so reviewers spend time cleaning it up. The team changes the output format to customer problem, decision maker, deadline, next action, and quoted evidence, and it tells the model not to infer missing facts. They test the prompt on 20 historical calls and record rejection reasons. Extraction time improves, but weak evidence remains a problem, so the next iteration retrieves CRM fields before generation. 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Documented failure patterns become regression tests when models, data, or workflows change. Teams can decide whether prompt changes are enough or whether RAG, tool use, or fine-tuning is required. Reusable templates reduce dependence on individual writing style. Documented failure patterns become regression tests when models, data, or workflows change.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "d5aeaab8bc0fc2ca5d8a10acac2b0b6923010f8bb66a995f0c94aca883540698" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:usage:abe614e1de96cf4d", "section_key": "usage", "heading": "使い方のポイント", "text": "Prompt engineering is input, constraint, and evaluation design, not just clever wording. A strong prompt states the goal, context, output format, prohibited behavior, and acceptance criteria. Improvement requires iteration against test cases and observed failures. Prompts alone do not guarantee factuality, permissions, or security. Business use needs templates, logs, and review ownership.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "abe614e1de96cf4d57ff89e0a75280357a58a0383ab3dd498e0684df1be1753c" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:drivers:29e591dcfeaa64de", "section_key": "drivers", "heading": "何が数字を動かすか", "text": "Prompt quality depends more on goal clarity, constraints, and evaluation criteria than on prompt length. 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Longer prompts are not automatically better; irrelevant context can reduce quality. Internal templates should show prohibited data and safe examples, not only ideal prompts. Verify important facts with trusted sources or systems of record. Longer prompts are not automatically better; irrelevant context can reduce quality. Internal templates should show prohibited data and safe examples, not only ideal prompts.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "3ef3a9c95e001544b62b43a4c9824f0d136e619b062f49b7a6d78ae46863791b" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:misunderstandings:64b4c67e840cd3d2", "section_key": "misunderstandings", "heading": "よくある誤解 / 落とし穴", "text": "There is no universal magic prompt. Production quality depends on tests and operations. Long prompts are not automatically good. Clear constraints matter more than volume. Prompts alone cannot provide security. Permissions, validation, UI, and logging still matter.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "64b4c67e840cd3d2bb282342a44a6b11af7549f014fcde7b664ca28f7853c3b2" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:examples:4c66d02cf2ac0b90", "section_key": "examples", "heading": "最小例", "text": "A revenue operations team builds a prompt to extract next actions from sales call notes. The first version returns too many low-value items, so reviewers spend time cleaning it up. The team changes the output format to customer problem, decision maker, deadline, next action, and quoted evidence, and it tells the model not to infer missing facts. They test the prompt on 20 historical calls and record rejection reasons. Extraction time improves, but weak evidence remains a problem, so the next iteration retrieves CRM fields before generation. Prompt work becomes one layer in a measurable workflow rather than a one-off instruction.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "4c66d02cf2ac0b90045906fceecf13c73c036a98517fe20681be0ade6e67a05c" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:comparisons:b73e4d38142a3f9a", "section_key": "comparisons", "heading": "似ている言葉との違い", "text": "Prompt Engineering | Designs the input | Fast and low-cost to iterate RAG | Retrieves external knowledge | Better when freshness and evidence matter Fine-tuning | Trains behavior | Better for consistent style or domain adaptation Prompt Engineering | Designs the input | Fast and low-cost to iterate RAG | Retrieves external knowledge | Better when freshness and evidence matter Fine-tuning | Trains behavior | Better for consistent style or domain adaptation", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "b73e4d38142a3f9aa52b74dcec91cf939e12ff09ffb26aaeba55f0a77f47271a" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:related_metrics:e32989fe2a64576e", "section_key": "related_metrics", "heading": "一緒に見る指標", "text": "Prompt work should be measured with evaluation and security concepts. AI Evaluation | Measures prompt changes | Keeps improvement empirical Prompt Injection | Attacks instructions through input | Critical when external text is used Fine-tuning | Adapts model behavior through training | Consider after prompt limits are clear AI Evaluation | Measures prompt changes | Keeps improvement empirical Prompt Injection | Attacks instructions through input | Critical when external text is used Fine-tuning | Adapts model behavior through training | Consider after prompt limits are clear", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "e32989fe2a64576ec4cad4cdf5b39ec623429905bd4d087613bd8b473a843d60" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:faq:35a84889ec40cc80", "section_key": "faq", "heading": "Who owns prompt engineering?", "text": "Who owns prompt engineering? AI specialists can help, but business owners need to define task success and review criteria.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "35a84889ec40cc80f9115c006358fef8bebabf468f6caf841f587d298790a977" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:faq:d744403be43281e7", "section_key": "faq", "heading": "Can a prompt guarantee facts?", "text": "Can a prompt guarantee facts? No. Important facts should be verified against trusted sources or systems of record.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "d744403be43281e7f874181f16f230718cf7576f45f3966f1ed8b014892a3015" }, { "chunk_id": "core:chunk:prompt-engineering:en-US:faq:6c6a9c3dfda7d00e", "section_key": "faq", "heading": "Should it come before fine-tuning?", "text": "Should it come before fine-tuning? Usually yes. Try prompts, evaluation, and retrieval before training a model for a specific behavior.", "source_refs": [ "concept:prompt-engineering:en-US", "concept:prompt-engineering:ja-JP", "core-product-update-ai-term-pack-v1:prompt-engineering" ], "content_hash": "6c6a9c3dfda7d00e5b419af8fa72bd55790a8c1e483c27f5fa89184a6d049e1e" } ], "content_hash": "b8f143da2499c14967bddc0aba20227be2af7585b034bcdfce0193be279d80fe", "hash_algorithm": "sha256", "rag": { "chunk_id_policy": "core:chunk:{canonical_slug}:{locale}:{section_key}:{sha256_16}", "content_hash_policy": "sha256_stable_json", "source_refs_precision": "source_object_ref_or_source_key_current_snapshot" }, "quality": { "content_tier": "db_backed", "quality": "reviewed", "publication_status": "published_reviewed", "source_count": 2, "last_reviewed_at": "2026-07-04T15:30:00.000Z", "trust_policy_version": "core-trust-policy-v1-2026-06-22" }, "limitations": { "policy_version": "core-trust-policy-v1-2026-06-22", "professional_advice_boundary": "reference_only_not_professional_advice", "text": "This page is reference information for research and learning. 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