{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DV6T6BMNAFNH7MKKP2VG7ICWL2","short_pith_number":"pith:DV6T6BMN","schema_version":"1.0","canonical_sha256":"1d7d3f058d015a7fb14a7eaa6fa0565eba286b17ab0d7dac2443af58731e8e4b","source":{"kind":"arxiv","id":"2409.08775","version":3},"attestation_state":"computed","paper":{"title":"What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Chenyang Yang, Hua Shen, Kenneth Koedinger, Qianou Ma, Tongshuang Wu, Weirui Peng","submitted_at":"2024-09-13T12:34:14Z","abstract_excerpt":"Prompting LLMs for complex tasks (e.g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e.g., \"start the response with a tl;dr\"). However, existing prompt engineering instructions often lack focused training on requirement articulation and instead tend to emphasize increasingly automatable strategies (e.g., tricks like adding role-plays and \"think step-by-step\"). To address the gap, we introduce Requirement-Oriented Prompt Engineering (ROPE), a paradigm that focuses human attention on generating clear, complete requirements during prompting. We impl"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2409.08775","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.HC","submitted_at":"2024-09-13T12:34:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9fcc8b28648488ffae87c3485ae68825460f46f43cd9aa7ecf0e9b221075f634","abstract_canon_sha256":"3909ba6c28b62497dc12fea0b3cd6c72067a6118b95a67f4515dc137be27adb7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:20.434945Z","signature_b64":"pzCEBUJj3mGWM6O11ZfS9M8HZBqqsKid4gvjeUkmRluJBdRQ7L7259da5HRtFWNMbeImIVxQbTrWcOG0Pp3tBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d7d3f058d015a7fb14a7eaa6fa0565eba286b17ab0d7dac2443af58731e8e4b","last_reissued_at":"2026-06-30T01:17:20.434199Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:20.434199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Chenyang Yang, Hua Shen, Kenneth Koedinger, Qianou Ma, Tongshuang Wu, Weirui Peng","submitted_at":"2024-09-13T12:34:14Z","abstract_excerpt":"Prompting LLMs for complex tasks (e.g., building a trip advisor chatbot) needs humans to clearly articulate customized requirements (e.g., \"start the response with a tl;dr\"). However, existing prompt engineering instructions often lack focused training on requirement articulation and instead tend to emphasize increasingly automatable strategies (e.g., tricks like adding role-plays and \"think step-by-step\"). To address the gap, we introduce Requirement-Oriented Prompt Engineering (ROPE), a paradigm that focuses human attention on generating clear, complete requirements during prompting. We impl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.08775","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2409.08775/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2409.08775","created_at":"2026-06-30T01:17:20.434293+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.08775v3","created_at":"2026-06-30T01:17:20.434293+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.08775","created_at":"2026-06-30T01:17:20.434293+00:00"},{"alias_kind":"pith_short_12","alias_value":"DV6T6BMNAFNH","created_at":"2026-06-30T01:17:20.434293+00:00"},{"alias_kind":"pith_short_16","alias_value":"DV6T6BMNAFNH7MKK","created_at":"2026-06-30T01:17:20.434293+00:00"},{"alias_kind":"pith_short_8","alias_value":"DV6T6BMN","created_at":"2026-06-30T01:17:20.434293+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2601.11848","citing_title":"Compass vs Railway Tracks: Unpacking User Mental Models for Communicating Long-Horizon Work to Humans vs. AI","ref_index":46,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11240","citing_title":"When to Ask a Question: Understanding Communication Strategies in Generative AI Tools","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2","json":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2.json","graph_json":"https://pith.science/api/pith-number/DV6T6BMNAFNH7MKKP2VG7ICWL2/graph.json","events_json":"https://pith.science/api/pith-number/DV6T6BMNAFNH7MKKP2VG7ICWL2/events.json","paper":"https://pith.science/paper/DV6T6BMN"},"agent_actions":{"view_html":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2","download_json":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2.json","view_paper":"https://pith.science/paper/DV6T6BMN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.08775&json=true","fetch_graph":"https://pith.science/api/pith-number/DV6T6BMNAFNH7MKKP2VG7ICWL2/graph.json","fetch_events":"https://pith.science/api/pith-number/DV6T6BMNAFNH7MKKP2VG7ICWL2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2/action/storage_attestation","attest_author":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2/action/author_attestation","sign_citation":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2/action/citation_signature","submit_replication":"https://pith.science/pith/DV6T6BMNAFNH7MKKP2VG7ICWL2/action/replication_record"}},"created_at":"2026-06-30T01:17:20.434293+00:00","updated_at":"2026-06-30T01:17:20.434293+00:00"}