{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AXF6EX43BJIUZML5RMLWIXYZZK","short_pith_number":"pith:AXF6EX43","schema_version":"1.0","canonical_sha256":"05cbe25f9b0a514cb17d8b17645f19cab0e32408354be78a27f9359de1847cfe","source":{"kind":"arxiv","id":"2409.12320","version":1},"attestation_state":"computed","paper":{"title":"The Effect of Education in Prompt Engineering: Evidence from Journalists","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Amirsiavosh Bashardoust, Dominique Geissler, Stefan Feuerriegel, Yash Raj Shrestha, Yuanjun Feng","submitted_at":"2024-09-18T21:28:49Z","abstract_excerpt":"Large language models (LLMs) are increasingly used in daily work. In this paper, we analyze whether training in prompt engineering can improve the interactions of users with LLMs. For this, we conducted a field experiment where we asked journalists to write short texts before and after training in prompt engineering. We then analyzed the effect of training on three dimensions: (1) the user experience of journalists when interacting with LLMs, (2) the accuracy of the texts (assessed by a domain expert), and (3) the reader perception, such as clarity, engagement, and other text quality dimension"},"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":"2409.12320","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-09-18T21:28:49Z","cross_cats_sorted":[],"title_canon_sha256":"0ff61612edfb9821dec03f85dc9a780ed859a86ad83ba807a6230e25327178bb","abstract_canon_sha256":"4a66f64e0f5eef04a2204c33b04ff1d09fe3d136199ff6dec0e07c7a5c7097b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:08:52.044726Z","signature_b64":"QKm4beW5BPHSPSuXQv7ff1PWpQyIqD3OExMlPYZ3wTfC3cyN3nuGC3uVUuFCE4Hizv2haaZqkGo5WchOP6NqBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05cbe25f9b0a514cb17d8b17645f19cab0e32408354be78a27f9359de1847cfe","last_reissued_at":"2026-07-05T09:08:52.044273Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:08:52.044273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Effect of Education in Prompt Engineering: Evidence from Journalists","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Amirsiavosh Bashardoust, Dominique Geissler, Stefan Feuerriegel, Yash Raj Shrestha, Yuanjun Feng","submitted_at":"2024-09-18T21:28:49Z","abstract_excerpt":"Large language models (LLMs) are increasingly used in daily work. In this paper, we analyze whether training in prompt engineering can improve the interactions of users with LLMs. For this, we conducted a field experiment where we asked journalists to write short texts before and after training in prompt engineering. We then analyzed the effect of training on three dimensions: (1) the user experience of journalists when interacting with LLMs, (2) the accuracy of the texts (assessed by a domain expert), and (3) the reader perception, such as clarity, engagement, and other text quality dimension"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.12320","kind":"arxiv","version":1},"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/2409.12320/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":"2409.12320","created_at":"2026-07-05T09:08:52.044334+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.12320v1","created_at":"2026-07-05T09:08:52.044334+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.12320","created_at":"2026-07-05T09:08:52.044334+00:00"},{"alias_kind":"pith_short_12","alias_value":"AXF6EX43BJIU","created_at":"2026-07-05T09:08:52.044334+00:00"},{"alias_kind":"pith_short_16","alias_value":"AXF6EX43BJIUZML5","created_at":"2026-07-05T09:08:52.044334+00:00"},{"alias_kind":"pith_short_8","alias_value":"AXF6EX43","created_at":"2026-07-05T09:08:52.044334+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28510","citing_title":"Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK","json":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK.json","graph_json":"https://pith.science/api/pith-number/AXF6EX43BJIUZML5RMLWIXYZZK/graph.json","events_json":"https://pith.science/api/pith-number/AXF6EX43BJIUZML5RMLWIXYZZK/events.json","paper":"https://pith.science/paper/AXF6EX43"},"agent_actions":{"view_html":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK","download_json":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK.json","view_paper":"https://pith.science/paper/AXF6EX43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.12320&json=true","fetch_graph":"https://pith.science/api/pith-number/AXF6EX43BJIUZML5RMLWIXYZZK/graph.json","fetch_events":"https://pith.science/api/pith-number/AXF6EX43BJIUZML5RMLWIXYZZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK/action/storage_attestation","attest_author":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK/action/author_attestation","sign_citation":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK/action/citation_signature","submit_replication":"https://pith.science/pith/AXF6EX43BJIUZML5RMLWIXYZZK/action/replication_record"}},"created_at":"2026-07-05T09:08:52.044334+00:00","updated_at":"2026-07-05T09:08:52.044334+00:00"}