{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OXJD54C4BPEYRHCXX4LUMZXDRR","short_pith_number":"pith:OXJD54C4","schema_version":"1.0","canonical_sha256":"75d23ef05c0bc9889c57bf174666e38c719d4e793c1d6ad5a3ebdf78e799bf9b","source":{"kind":"arxiv","id":"2310.14735","version":6},"attestation_state":"computed","paper":{"title":"Unleashing the potential of prompt engineering for large language models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Banghao Chen, Nicolas Langren\\'e, Shengxin Zhu, Zhaofeng Zhang","submitted_at":"2023-10-23T09:15:18Z","abstract_excerpt":"This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o and Claude-3, and in Vision-Language Models (VLMs), with models such as CLIP and ALIGN. Prompt engineering is the process of structuring inputs, which has emerged as a crucial technique to maximize the utility and accuracy of these models. "},"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":"2310.14735","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T09:15:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b28a7c8c079744ec9f7b8f02f26b7bb7b15acc41b9f62308128cdc7e56c32e7","abstract_canon_sha256":"b94c3094440cedf900e0452b119d720530807dcdc5dec881fc1a4f75d3bf3510"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:29.002869Z","signature_b64":"POHtrxE4aUsnS5JeHInaKhgi83KpX+NgBMhHINyJ9jw7hZpc/G9YqNpvOCNLg6N1hJ/ZXXhcEh/GGlqbIAmYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75d23ef05c0bc9889c57bf174666e38c719d4e793c1d6ad5a3ebdf78e799bf9b","last_reissued_at":"2026-07-05T11:22:29.002375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:29.002375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unleashing the potential of prompt engineering for large language models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Banghao Chen, Nicolas Langren\\'e, Shengxin Zhu, Zhaofeng Zhang","submitted_at":"2023-10-23T09:15:18Z","abstract_excerpt":"This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence (AI), from its inception in the 1950s to the emergence of advanced neural networks and deep learning architectures, has made a breakthrough in LLMs, with models such as GPT-4o and Claude-3, and in Vision-Language Models (VLMs), with models such as CLIP and ALIGN. Prompt engineering is the process of structuring inputs, which has emerged as a crucial technique to maximize the utility and accuracy of these models. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14735","kind":"arxiv","version":6},"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/2310.14735/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":"2310.14735","created_at":"2026-07-05T11:22:29.002437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.14735v6","created_at":"2026-07-05T11:22:29.002437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14735","created_at":"2026-07-05T11:22:29.002437+00:00"},{"alias_kind":"pith_short_12","alias_value":"OXJD54C4BPEY","created_at":"2026-07-05T11:22:29.002437+00:00"},{"alias_kind":"pith_short_16","alias_value":"OXJD54C4BPEYRHCX","created_at":"2026-07-05T11:22:29.002437+00:00"},{"alias_kind":"pith_short_8","alias_value":"OXJD54C4","created_at":"2026-07-05T11:22:29.002437+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01691","citing_title":"IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30989","citing_title":"Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG","ref_index":77,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24171","citing_title":"PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24300","citing_title":"Enhancing Reliability in LLM-Based Secure Code Generation","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2506.16345","citing_title":"Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2508.14823","citing_title":"Using an LLM to Investigate Students' Explanations on Conceptual Physics Questions","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2512.11013","citing_title":"PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2408.08435","citing_title":"Automated Design of Agentic Systems","ref_index":137,"is_internal_anchor":false},{"citing_arxiv_id":"2402.17177","citing_title":"Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11163","citing_title":"Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2402.07927","citing_title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR","json":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR.json","graph_json":"https://pith.science/api/pith-number/OXJD54C4BPEYRHCXX4LUMZXDRR/graph.json","events_json":"https://pith.science/api/pith-number/OXJD54C4BPEYRHCXX4LUMZXDRR/events.json","paper":"https://pith.science/paper/OXJD54C4"},"agent_actions":{"view_html":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR","download_json":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR.json","view_paper":"https://pith.science/paper/OXJD54C4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.14735&json=true","fetch_graph":"https://pith.science/api/pith-number/OXJD54C4BPEYRHCXX4LUMZXDRR/graph.json","fetch_events":"https://pith.science/api/pith-number/OXJD54C4BPEYRHCXX4LUMZXDRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/action/storage_attestation","attest_author":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/action/author_attestation","sign_citation":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/action/citation_signature","submit_replication":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/action/replication_record"}},"created_at":"2026-07-05T11:22:29.002437+00:00","updated_at":"2026-07-05T11:22:29.002437+00:00"}