{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SMTC5334IR6NDJBAWEYZ7NEMF4","short_pith_number":"pith:SMTC5334","canonical_record":{"source":{"id":"2410.12812","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2024-10-01T03:54:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aee820ba6b84b1199f34d929535240981104bd63b24b91278a1a9c08a7a9ac3a","abstract_canon_sha256":"b64b2ebab6ef3043b693524a8bea91183354c074594853409f494e1d1a240fe8"},"schema_version":"1.0"},"canonical_sha256":"93262eef7c447cd1a420b1319fb48c2f0dadc43613e9b8723b074b1bcaaf92fa","source":{"kind":"arxiv","id":"2410.12812","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12812","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12812v1","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12812","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"SMTC5334IR6N","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"pith_short_16","alias_value":"SMTC5334IR6NDJBA","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"pith_short_8","alias_value":"SMTC5334","created_at":"2026-07-05T09:21:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SMTC5334IR6NDJBAWEYZ7NEMF4","target":"record","payload":{"canonical_record":{"source":{"id":"2410.12812","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2024-10-01T03:54:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"aee820ba6b84b1199f34d929535240981104bd63b24b91278a1a9c08a7a9ac3a","abstract_canon_sha256":"b64b2ebab6ef3043b693524a8bea91183354c074594853409f494e1d1a240fe8"},"schema_version":"1.0"},"canonical_sha256":"93262eef7c447cd1a420b1319fb48c2f0dadc43613e9b8723b074b1bcaaf92fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:51.281419Z","signature_b64":"u4RQsp4SEpz41Z2ly1B+LkF+cV916uaXiaM0fvnyMbouBd/XWsWYi3+07wB9JWJgp8Kb2hDkt8MdY7pKL2b/Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93262eef7c447cd1a420b1319fb48c2f0dadc43613e9b8723b074b1bcaaf92fa","last_reissued_at":"2026-07-05T09:21:51.280933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:51.280933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.12812","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1alcTcaHDkWbuseaK7X1FCXSsh4z8DXKPYQoAjOHK7AexCJ9a7EZLsIFIJ3l++/6jCih6o/CvIT9OnYD8PwPDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:53:12.219206Z"},"content_sha256":"5ac5bbe653b9cd5af2599a53cc4b56a3dae6d2f757e7060157eb2ad205f98434","schema_version":"1.0","event_id":"sha256:5ac5bbe653b9cd5af2599a53cc4b56a3dae6d2f757e7060157eb2ad205f98434"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SMTC5334IR6NDJBAWEYZ7NEMF4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Inge Halilovic, Jenifer Schlotfeldt, Sarah Packowski, Trish Smith","submitted_at":"2024-10-01T03:54:45Z","abstract_excerpt":"Retrieval-augmented generation (RAG) is a popular technique for using large language models (LLMs) to build customer-support, question-answering solutions. In this paper, we share our team's practical experience building and maintaining enterprise-scale RAG solutions that answer users' questions about our software based on product documentation. Our experience has not always matched the most common patterns in the RAG literature. This paper focuses on solution strategies that are modular and model-agnostic. For example, our experience over the past few years - using different search methods an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12812","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/2410.12812/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":"2026-07-05T09:21:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xJYzAahWQ1+X0l66gyYS8AE9qi7gXGOamH0++Gh5hIld4bscRX9U3uoOUs8ybRMRWfXYouWEPSm6WnLDBPTlCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T19:53:12.219684Z"},"content_sha256":"0f800af7fd40c1679f728170fd721629fedf184bb8ff355dc1f49bf53f8b74dd","schema_version":"1.0","event_id":"sha256:0f800af7fd40c1679f728170fd721629fedf184bb8ff355dc1f49bf53f8b74dd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SMTC5334IR6NDJBAWEYZ7NEMF4/bundle.json","state_url":"https://pith.science/pith/SMTC5334IR6NDJBAWEYZ7NEMF4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SMTC5334IR6NDJBAWEYZ7NEMF4/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":"2026-08-07T19:53:12Z","links":{"resolver":"https://pith.science/pith/SMTC5334IR6NDJBAWEYZ7NEMF4","bundle":"https://pith.science/pith/SMTC5334IR6NDJBAWEYZ7NEMF4/bundle.json","state":"https://pith.science/pith/SMTC5334IR6NDJBAWEYZ7NEMF4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SMTC5334IR6NDJBAWEYZ7NEMF4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SMTC5334IR6NDJBAWEYZ7NEMF4","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":"b64b2ebab6ef3043b693524a8bea91183354c074594853409f494e1d1a240fe8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2024-10-01T03:54:45Z","title_canon_sha256":"aee820ba6b84b1199f34d929535240981104bd63b24b91278a1a9c08a7a9ac3a"},"schema_version":"1.0","source":{"id":"2410.12812","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.12812","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"arxiv_version","alias_value":"2410.12812v1","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.12812","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"pith_short_12","alias_value":"SMTC5334IR6N","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"pith_short_16","alias_value":"SMTC5334IR6NDJBA","created_at":"2026-07-05T09:21:51Z"},{"alias_kind":"pith_short_8","alias_value":"SMTC5334","created_at":"2026-07-05T09:21:51Z"}],"graph_snapshots":[{"event_id":"sha256:0f800af7fd40c1679f728170fd721629fedf184bb8ff355dc1f49bf53f8b74dd","target":"graph","created_at":"2026-07-05T09:21:51Z","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/2410.12812/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-augmented generation (RAG) is a popular technique for using large language models (LLMs) to build customer-support, question-answering solutions. In this paper, we share our team's practical experience building and maintaining enterprise-scale RAG solutions that answer users' questions about our software based on product documentation. Our experience has not always matched the most common patterns in the RAG literature. This paper focuses on solution strategies that are modular and model-agnostic. For example, our experience over the past few years - using different search methods an","authors_text":"Inge Halilovic, Jenifer Schlotfeldt, Sarah Packowski, Trish Smith","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2024-10-01T03:54:45Z","title":"Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.12812","kind":"arxiv","version":1},"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":"sha256:5ac5bbe653b9cd5af2599a53cc4b56a3dae6d2f757e7060157eb2ad205f98434","target":"record","created_at":"2026-07-05T09:21:51Z","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":"b64b2ebab6ef3043b693524a8bea91183354c074594853409f494e1d1a240fe8","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.IR","submitted_at":"2024-10-01T03:54:45Z","title_canon_sha256":"aee820ba6b84b1199f34d929535240981104bd63b24b91278a1a9c08a7a9ac3a"},"schema_version":"1.0","source":{"id":"2410.12812","kind":"arxiv","version":1}},"canonical_sha256":"93262eef7c447cd1a420b1319fb48c2f0dadc43613e9b8723b074b1bcaaf92fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"93262eef7c447cd1a420b1319fb48c2f0dadc43613e9b8723b074b1bcaaf92fa","first_computed_at":"2026-07-05T09:21:51.280933Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:21:51.280933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u4RQsp4SEpz41Z2ly1B+LkF+cV916uaXiaM0fvnyMbouBd/XWsWYi3+07wB9JWJgp8Kb2hDkt8MdY7pKL2b/Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:21:51.281419Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.12812","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ac5bbe653b9cd5af2599a53cc4b56a3dae6d2f757e7060157eb2ad205f98434","sha256:0f800af7fd40c1679f728170fd721629fedf184bb8ff355dc1f49bf53f8b74dd"],"state_sha256":"d87246d0edf3dd4d4e6fd9b8f49fc060a1e10344581b3a87ffb518475d6e3f77"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rwHmKzLRwZpxFkQ6APe9Tu0/+cTcDSY8Yxg81x8vIg0YLEFk96nJ33ulOETiNV1qQlXn02cSAlHjkw8qe9bxDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T19:53:12.222579Z","bundle_sha256":"2bd59168bfc4dfb380cf173c39ccb28811c6ac86d5bff6687f861334a433a423"}}