{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HN5MHT7SLKMKNZSWXVGD56NJ5L","short_pith_number":"pith:HN5MHT7S","schema_version":"1.0","canonical_sha256":"3b7ac3cff25a98a6e656bd4c3ef9a9eacaeaf477622cb1d758e5d079ef861015","source":{"kind":"arxiv","id":"2409.03708","version":2},"attestation_state":"computed","paper":{"title":"RAG based Question-Answering for Contextual Response Prediction System","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Nafis Irtiza Tripto, Nian Yan, Reshma Lal Jagadheesh, Saurabh Vaichal, Sriram Veturi","submitted_at":"2024-09-05T17:14:23Z","abstract_excerpt":"Large Language Models (LLMs) have shown versatility in various Natural Language Processing (NLP) tasks, including their potential as effective question-answering systems. However, to provide precise and relevant information in response to specific customer queries in industry settings, LLMs require access to a comprehensive knowledge base to avoid hallucinations. Retrieval Augmented Generation (RAG) emerges as a promising technique to address this challenge. Yet, developing an accurate question-answering framework for real-world applications using RAG entails several challenges: 1) data availa"},"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":"2409.03708","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-05T17:14:23Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"0e8f4964a86218838af65174a6386bbd35f032a50ad1e18b55369d3e58847e04","abstract_canon_sha256":"5338a4fd23648c52d1bc00b9b8e5c5dcf233ee0aaf73ba5203fe7b4ce1539ee5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:56.542829Z","signature_b64":"vKlgKRVlCrbmlgu7Gy7brGPAGtGnoaIjpdkEjEBIxNA427wI0k8BuUTY6u9QWK3o1F71HBhL9Y9OWJu9Nz0KAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b7ac3cff25a98a6e656bd4c3ef9a9eacaeaf477622cb1d758e5d079ef861015","last_reissued_at":"2026-07-05T09:03:56.542355Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:56.542355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RAG based Question-Answering for Contextual Response Prediction System","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Nafis Irtiza Tripto, Nian Yan, Reshma Lal Jagadheesh, Saurabh Vaichal, Sriram Veturi","submitted_at":"2024-09-05T17:14:23Z","abstract_excerpt":"Large Language Models (LLMs) have shown versatility in various Natural Language Processing (NLP) tasks, including their potential as effective question-answering systems. However, to provide precise and relevant information in response to specific customer queries in industry settings, LLMs require access to a comprehensive knowledge base to avoid hallucinations. Retrieval Augmented Generation (RAG) emerges as a promising technique to address this challenge. Yet, developing an accurate question-answering framework for real-world applications using RAG entails several challenges: 1) data availa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03708","kind":"arxiv","version":2},"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/2409.03708/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":"2409.03708","created_at":"2026-07-05T09:03:56.542415+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.03708v2","created_at":"2026-07-05T09:03:56.542415+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03708","created_at":"2026-07-05T09:03:56.542415+00:00"},{"alias_kind":"pith_short_12","alias_value":"HN5MHT7SLKMK","created_at":"2026-07-05T09:03:56.542415+00:00"},{"alias_kind":"pith_short_16","alias_value":"HN5MHT7SLKMKNZSW","created_at":"2026-07-05T09:03:56.542415+00:00"},{"alias_kind":"pith_short_8","alias_value":"HN5MHT7S","created_at":"2026-07-05T09:03:56.542415+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12370","citing_title":"Context Convergence Improves Answering Inferential Questions","ref_index":35,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L","json":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L.json","graph_json":"https://pith.science/api/pith-number/HN5MHT7SLKMKNZSWXVGD56NJ5L/graph.json","events_json":"https://pith.science/api/pith-number/HN5MHT7SLKMKNZSWXVGD56NJ5L/events.json","paper":"https://pith.science/paper/HN5MHT7S"},"agent_actions":{"view_html":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L","download_json":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L.json","view_paper":"https://pith.science/paper/HN5MHT7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.03708&json=true","fetch_graph":"https://pith.science/api/pith-number/HN5MHT7SLKMKNZSWXVGD56NJ5L/graph.json","fetch_events":"https://pith.science/api/pith-number/HN5MHT7SLKMKNZSWXVGD56NJ5L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L/action/storage_attestation","attest_author":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L/action/author_attestation","sign_citation":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L/action/citation_signature","submit_replication":"https://pith.science/pith/HN5MHT7SLKMKNZSWXVGD56NJ5L/action/replication_record"}},"created_at":"2026-07-05T09:03:56.542415+00:00","updated_at":"2026-07-05T09:03:56.542415+00:00"}