{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZZDP63AGZIYGYRHHP3VSEMR4IJ","short_pith_number":"pith:ZZDP63AG","schema_version":"1.0","canonical_sha256":"ce46ff6c06ca306c44e77eeb22323c4279768e3d8c616df2d211337c5e29758c","source":{"kind":"arxiv","id":"2308.03854","version":1},"attestation_state":"computed","paper":{"title":"Revisiting Prompt Engineering via Declarative Crowdsourcing","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"cs.DB","authors_text":"Aditya G. Parameswaran, Naman Jain, Parth Asawa, Shreya Shankar, Yujie Wang","submitted_at":"2023-08-07T18:04:12Z","abstract_excerpt":"Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits and recipes centered around so-called prompt engineering-the process of asking an LLM to do something via a series of prompts. However, for LLM-powered data processing workflows, in particular, optimizing for quality, while keeping cost bounded, is a tedious, manual process. We put forth a vision for declarative prompt engineering. We view LLMs like crowd workers and leverage ideas from the declarative crowdsourcing"},"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":"2308.03854","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DB","submitted_at":"2023-08-07T18:04:12Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"8a315517b9e39e741eb3f1c228231ff8e01aa555cf0032d2c5cbdb91cd0b8f08","abstract_canon_sha256":"8c3cd365caab552b0ada82e80dd2ddf56c5e2e4346f3eea75938f3cc362a83c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:39:18.289801Z","signature_b64":"hwKjP0OY3gJiJegkPjIf6R/E+54lMzpUCNtRfxMrhPaEKv4/hb35Xxig2+bscpmOyhKJrzE10X5dizB2TtwsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce46ff6c06ca306c44e77eeb22323c4279768e3d8c616df2d211337c5e29758c","last_reissued_at":"2026-07-05T06:39:18.289310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:39:18.289310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Revisiting Prompt Engineering via Declarative Crowdsourcing","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"cs.DB","authors_text":"Aditya G. Parameswaran, Naman Jain, Parth Asawa, Shreya Shankar, Yujie Wang","submitted_at":"2023-08-07T18:04:12Z","abstract_excerpt":"Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits and recipes centered around so-called prompt engineering-the process of asking an LLM to do something via a series of prompts. However, for LLM-powered data processing workflows, in particular, optimizing for quality, while keeping cost bounded, is a tedious, manual process. We put forth a vision for declarative prompt engineering. We view LLMs like crowd workers and leverage ideas from the declarative crowdsourcing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.03854","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/2308.03854/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":"2308.03854","created_at":"2026-07-05T06:39:18.289378+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.03854v1","created_at":"2026-07-05T06:39:18.289378+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.03854","created_at":"2026-07-05T06:39:18.289378+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZZDP63AGZIYG","created_at":"2026-07-05T06:39:18.289378+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZZDP63AGZIYGYRHH","created_at":"2026-07-05T06:39:18.289378+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZZDP63AG","created_at":"2026-07-05T06:39:18.289378+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02655","citing_title":"Semantic Data Processing with Holistic Data Understanding","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ","json":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ.json","graph_json":"https://pith.science/api/pith-number/ZZDP63AGZIYGYRHHP3VSEMR4IJ/graph.json","events_json":"https://pith.science/api/pith-number/ZZDP63AGZIYGYRHHP3VSEMR4IJ/events.json","paper":"https://pith.science/paper/ZZDP63AG"},"agent_actions":{"view_html":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ","download_json":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ.json","view_paper":"https://pith.science/paper/ZZDP63AG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.03854&json=true","fetch_graph":"https://pith.science/api/pith-number/ZZDP63AGZIYGYRHHP3VSEMR4IJ/graph.json","fetch_events":"https://pith.science/api/pith-number/ZZDP63AGZIYGYRHHP3VSEMR4IJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ/action/storage_attestation","attest_author":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ/action/author_attestation","sign_citation":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ/action/citation_signature","submit_replication":"https://pith.science/pith/ZZDP63AGZIYGYRHHP3VSEMR4IJ/action/replication_record"}},"created_at":"2026-07-05T06:39:18.289378+00:00","updated_at":"2026-07-05T06:39:18.289378+00:00"}