{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OXJD54C4BPEYRHCXX4LUMZXDRR","short_pith_number":"pith:OXJD54C4","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"},"canonical_sha256":"75d23ef05c0bc9889c57bf174666e38c719d4e793c1d6ad5a3ebdf78e799bf9b","source":{"kind":"arxiv","id":"2310.14735","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14735","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14735v6","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14735","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"pith_short_12","alias_value":"OXJD54C4BPEY","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"pith_short_16","alias_value":"OXJD54C4BPEYRHCX","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"pith_short_8","alias_value":"OXJD54C4","created_at":"2026-07-05T11:22:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OXJD54C4BPEYRHCXX4LUMZXDRR","target":"record","payload":{"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"},"canonical_sha256":"75d23ef05c0bc9889c57bf174666e38c719d4e793c1d6ad5a3ebdf78e799bf9b","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"},"source_kind":"arxiv","source_id":"2310.14735","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:22:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NvZ2rmcoYeDV1ZktvVibcbTu2HOVW6ZA2TYux6wvYhauDO971M7FTf0rR1g+6LQFZmuk4QP+7iaAoP7dy8ngAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T20:59:29.004655Z"},"content_sha256":"5dce73ed4520e70fda43253fd5adc591db73a5da4300d4fe7489e0445a2fb174","schema_version":"1.0","event_id":"sha256:5dce73ed4520e70fda43253fd5adc591db73a5da4300d4fe7489e0445a2fb174"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OXJD54C4BPEYRHCXX4LUMZXDRR","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:22:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Jms1eWeH9/JaBoLKDzZObyWi9DkljLwBfb8JtdQsVSTLdZCfVP9+3WiweMEsRVVGy++vzSYN/XGRoROO7sgDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T20:59:29.005165Z"},"content_sha256":"23f9e518f1d88c21d7a0c2a9aa3bb97a809d339dd419607b8736c26d24089c09","schema_version":"1.0","event_id":"sha256:23f9e518f1d88c21d7a0c2a9aa3bb97a809d339dd419607b8736c26d24089c09"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/bundle.json","state_url":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T20:59:29Z","links":{"resolver":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR","bundle":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/bundle.json","state":"https://pith.science/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OXJD54C4BPEYRHCXX4LUMZXDRR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OXJD54C4BPEYRHCXX4LUMZXDRR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b94c3094440cedf900e0452b119d720530807dcdc5dec881fc1a4f75d3bf3510","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T09:15:18Z","title_canon_sha256":"7b28a7c8c079744ec9f7b8f02f26b7bb7b15acc41b9f62308128cdc7e56c32e7"},"schema_version":"1.0","source":{"id":"2310.14735","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14735","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14735v6","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14735","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"pith_short_12","alias_value":"OXJD54C4BPEY","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"pith_short_16","alias_value":"OXJD54C4BPEYRHCX","created_at":"2026-07-05T11:22:29Z"},{"alias_kind":"pith_short_8","alias_value":"OXJD54C4","created_at":"2026-07-05T11:22:29Z"}],"graph_snapshots":[{"event_id":"sha256:23f9e518f1d88c21d7a0c2a9aa3bb97a809d339dd419607b8736c26d24089c09","target":"graph","created_at":"2026-07-05T11:22:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.14735/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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. ","authors_text":"Banghao Chen, Nicolas Langren\\'e, Shengxin Zhu, Zhaofeng Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T09:15:18Z","title":"Unleashing the potential of prompt engineering for large language models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14735","kind":"arxiv","version":6},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5dce73ed4520e70fda43253fd5adc591db73a5da4300d4fe7489e0445a2fb174","target":"record","created_at":"2026-07-05T11:22:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b94c3094440cedf900e0452b119d720530807dcdc5dec881fc1a4f75d3bf3510","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-23T09:15:18Z","title_canon_sha256":"7b28a7c8c079744ec9f7b8f02f26b7bb7b15acc41b9f62308128cdc7e56c32e7"},"schema_version":"1.0","source":{"id":"2310.14735","kind":"arxiv","version":6}},"canonical_sha256":"75d23ef05c0bc9889c57bf174666e38c719d4e793c1d6ad5a3ebdf78e799bf9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75d23ef05c0bc9889c57bf174666e38c719d4e793c1d6ad5a3ebdf78e799bf9b","first_computed_at":"2026-07-05T11:22:29.002375Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:29.002375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"POHtrxE4aUsnS5JeHInaKhgi83KpX+NgBMhHINyJ9jw7hZpc/G9YqNpvOCNLg6N1hJ/ZXXhcEh/GGlqbIAmYAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:29.002869Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.14735","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5dce73ed4520e70fda43253fd5adc591db73a5da4300d4fe7489e0445a2fb174","sha256:23f9e518f1d88c21d7a0c2a9aa3bb97a809d339dd419607b8736c26d24089c09"],"state_sha256":"07273d9d20e0eac2858dc65ed939b7651b19eaf90cf7ccd0d6a0e45caef54849"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yulDIC1slK286JgP+PZY2oBmOaNeToUt0WZfoyAFCtFt4aJdBwvyfrpxDkl4Eol9xFpU44sMN9wlpaeEV4NfDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T20:59:29.009001Z","bundle_sha256":"9739207962035b655bf6714c85412ac506332569e475bca646c0a44c547f36db"}}