Paper Citation Record · LEDGER

Themes of Building LLM-based Applications for Production: A Practitioner's View

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2411.08574.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.08574 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:35:23.199239Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy21
unresolved19
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 683e7ae4-798f-4cf6-a985-b0fb0e7c23a2 · outbound

This paper cites Competition- level code generation with alphacode,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Competition- level code generation with alphacode,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:23.045831Z

Source-reported events for the cited work

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Observation d36a83c2-4dca-42af-9d31-c66c861e7213 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Themes of Building LLM-based Applications for Production: A Practitioner's View Evaluating Large Language Models Trained on Code

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:23.050500Z

Source-reported events for the cited work

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Observation d02d8625-1221-4097-88df-254dab5debf5 · outbound

This paper cites Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,

Reference 3

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Source-reported events for the cited work

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Observation 9f02777c-870e-47e0-b066-a13b97dfb1db · outbound

This paper cites Nuances are the key: Unlocking chatgpt to find failure- inducing tests with differential prompting,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Nuances are the key: Unlocking chatgpt to find failure- inducing tests with differential prompting,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.687179Z

Source-reported events for the cited work

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Observation 6e891c64-006d-43c3-a1d2-439acefc490b · outbound

This paper cites Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning,

Reference 5

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Source-reported events for the cited work

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Observation 8df5048f-458c-4621-8532-0f71459c1f80 · outbound

This paper cites Navigating challenges and technical debt in large language models deployment,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Navigating challenges and technical debt in large language models deployment,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.666422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31634c28-95d3-4e3b-95fe-d176a793fa5e · outbound

This paper cites Building Your Own Product Copilot: Challenges, Opportunities, and Needs.

Themes of Building LLM-based Applications for Production: A Practitioner's View Building Your Own Product Copilot: Challenges, Opportunities, and Needs

Reference 7

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Source-reported events for the cited work

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Observation dd78cac7-1e7f-42e7-8f46-245ea1642e62 · outbound

This paper cites Socio-technical anti-patterns in building ml-enabled software: Insights from leaders on the forefront,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Socio-technical anti-patterns in building ml-enabled software: Insights from leaders on the forefront,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.653116Z

Source-reported events for the cited work

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Observation 9f2b75b3-3630-4a42-9cae-b2002fccb21e · outbound

This paper cites Collaboration challenges in building ml-enabled systems: Communication, documentation, en- gineering, and process,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Collaboration challenges in building ml-enabled systems: Communication, documentation, en- gineering, and process,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.641565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 500c6326-6cff-4d5c-9c5c-0aa3a335420d · outbound

This paper cites Software engineering for machine learning: A case study,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Software engineering for machine learning: A case study,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.630812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 24d5f123-460c-4930-b389-da28b1be0b8f · outbound

This paper cites Data scientists in software teams: State of the art and challenges,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Data scientists in software teams: State of the art and challenges,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.618617Z

Source-reported events for the cited work

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Observation 6d8edab3-d294-4c53-aed2-ba561f57697d · outbound

This paper cites Hidden technical debt in machine learning systems,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Hidden technical debt in machine learning systems,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.606982Z

Source-reported events for the cited work

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Observation d342af83-66d0-4979-95d1-7a9fa6d1aefd · outbound

This paper cites How does machine learning change software development practices?.

Themes of Building LLM-based Applications for Production: A Practitioner's View How does machine learning change software development practices?

Reference 13

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Source-reported events for the cited work

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Observation ec5ffb90-6059-464a-b194-1dedb4f34dae · outbound

This paper cites Software engineering challenges of deep learning,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Software engineering challenges of deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.584491Z

Source-reported events for the cited work

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Observation aa2e2f76-3e60-4fbf-9329-dc67c9992499 · outbound

This paper cites How do engineers perceive difficulties in engineering of machine-learning systems?-questionnaire survey,.

Themes of Building LLM-based Applications for Production: A Practitioner's View How do engineers perceive difficulties in engineering of machine-learning systems?-questionnaire survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.573722Z

Source-reported events for the cited work

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Observation 1a6bf6c1-1c61-4148-9409-5eb30bdf6b9b · outbound

This paper cites A taxonomy of software engineering challenges for machine learning systems: An empirical investigation,.

Themes of Building LLM-based Applications for Production: A Practitioner's View A taxonomy of software engineering challenges for machine learning systems: An empirical investigation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.561845Z

Source-reported events for the cited work

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Observation 0617e607-0acd-4aa0-8245-cb4561c4af0e · outbound

This paper cites A meta- summary of challenges in building products with ml components– collecting experiences from 4758+ practitioners,.

Themes of Building LLM-based Applications for Production: A Practitioner's View A meta- summary of challenges in building products with ml components– collecting experiences from 4758+ practitioners,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.550685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 09d81551-4896-4f3f-9691-5a87f97a68de · outbound

This paper cites Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts.

Themes of Building LLM-based Applications for Production: A Practitioner's View Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts

Reference 18

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Source-reported events for the cited work

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Observation 2ed1ce6d-bfc0-4bf6-92cd-4dbfc4fe19ab · outbound

This paper cites Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers.

Themes of Building LLM-based Applications for Production: A Practitioner's View Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers

Reference 19

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Source-reported events for the cited work

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Observation cc621cf3-3031-4ed1-a34e-ae7debc38775 · outbound

This paper cites Seven failure points when engineering a retrieval augmented generation system,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Seven failure points when engineering a retrieval augmented generation system,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.538977Z

Source-reported events for the cited work

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Observation 0e7d759d-84bf-4d98-8a68-e44515b968f4 · outbound

This paper cites Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,

Reference 21

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Source-reported events for the cited work

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Observation c72ccd54-2c9c-4a1c-a27e-fe2a43314ece · outbound

This paper cites Robust Speech Recognition via Large-Scale Weak Supervision.

Themes of Building LLM-based Applications for Production: A Practitioner's View Robust Speech Recognition via Large-Scale Weak Supervision

Reference 22

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Source-reported events for the cited work

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Observation aba9ce2a-f505-4de3-8430-a6b215998d48 · outbound

This paper cites Pyannote.audio: Neural building blocks for speaker diarization.

Themes of Building LLM-based Applications for Production: A Practitioner's View Pyannote.audio: Neural building blocks for speaker diarization

Reference 23

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Observation d959eba6-70d5-481b-a2d9-e0147a0ac316 · outbound

This paper cites text-splitter,.

Themes of Building LLM-based Applications for Production: A Practitioner's View text-splitter,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8969d8b-27bd-4e78-bd3a-4a612147f28f · outbound

This paper cites BERTopic: Neural topic modeling with a class-based TF-IDF procedure.

Themes of Building LLM-based Applications for Production: A Practitioner's View BERTopic: Neural topic modeling with a class-based TF-IDF procedure

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:23.138984Z

Source-reported events for the cited work

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Observation 9da3b548-4e67-4455-9f7c-4e8c02a0d27a · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

Themes of Building LLM-based Applications for Production: A Practitioner's View Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 26

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Source-reported events for the cited work

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Observation 4f0c90cc-18e8-4acf-8332-f21be4d840b1 · outbound

This paper cites A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation.

Themes of Building LLM-based Applications for Production: A Practitioner's View A Methodology for Evaluating RAG Systems: A Case Study On Configuration Dependency Validation

Reference 27

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Source-reported events for the cited work

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Observation 7767f6e7-8798-411a-85cf-d1122ae1300b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Themes of Building LLM-based Applications for Production: A Practitioner's View LoRA: Low-Rank Adaptation of Large Language Models

Reference 28

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Observation b22df240-ce7b-45a3-b219-96c331f75217 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Themes of Building LLM-based Applications for Production: A Practitioner's View QLoRA: Efficient Finetuning of Quantized LLMs

Reference 29

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Themes of Building LLM-based Applications for Production: A Practitioner's View Exploring hyperparameter usage and tuning in machine learning research,

Reference 30

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Source-reported events for the cited work

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Observation fcf13e47-233c-4cbe-95d1-d6f0c529a8b9 · outbound

This paper cites Prompt programming for large language models: Beyond the few-shot paradigm,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Prompt programming for large language models: Beyond the few-shot paradigm,

Reference 31

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This paper cites Scaling Laws for Neural Language Models.

Themes of Building LLM-based Applications for Production: A Practitioner's View Scaling Laws for Neural Language Models

Reference 32

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Source-reported events for the cited work

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Observation 669f0c00-1a31-4a52-9d7e-c3d3e60c1a59 · outbound

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Themes of Building LLM-based Applications for Production: A Practitioner's View Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:23.169982Z

Source-reported events for the cited work

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Observation 7e6724e2-eaee-4ddd-831e-9e3ace23148a · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Themes of Building LLM-based Applications for Production: A Practitioner's View A Survey on Knowledge Distillation of Large Language Models

Reference 34

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Source-reported events for the cited work

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Observation bb719644-e89c-42bd-89fc-79750d48ee76 · outbound

This paper cites Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Who validates the validators? aligning llm-assisted evaluation of llm outputs with human preferences,

Reference 35

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Source-reported events for the cited work

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Observation 2d078342-8bc9-478e-b86a-b24301148d6a · outbound

This paper cites Openp5: An open-source platform for developing, training, and evaluating llm-based recommender systems,.

Themes of Building LLM-based Applications for Production: A Practitioner's View Openp5: An open-source platform for developing, training, and evaluating llm-based recommender systems,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.462825Z

Source-reported events for the cited work

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Observation 43c22f5d-f6b6-46c4-8797-451217c6b20e · outbound

This paper cites Software engineering using autonomous agents: Are we there yet?.

Themes of Building LLM-based Applications for Production: A Practitioner's View Software engineering using autonomous agents: Are we there yet?

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 43912122-b8df-449c-9a94-b0e7122ce5dc · outbound

This paper cites Building LLM Applications for Production - AI Campus Berlin.

Themes of Building LLM-based Applications for Production: A Practitioner's View Building LLM Applications for Production - AI Campus Berlin

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.411896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 38d8aada-a9ac-4ed3-8ccd-4e292e8d59a7 · outbound

This paper cites Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - 694.

Themes of Building LLM-based Applications for Production: A Practitioner's View Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - 694

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.398786Z

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Observation 8befc256-2a85-4616-bc88-6942889470fa · outbound

This paper cites LLM on K8s // Panel 2 // LLMs in Conference in Production Conference Part 2.

Themes of Building LLM-based Applications for Production: A Practitioner's View LLM on K8s // Panel 2 // LLMs in Conference in Production Conference Part 2

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.438439Z

Source-reported events for the cited work

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Observation ec64cbae-47d3-41a2-b95b-24c00bf55e08 · outbound

This paper cites LLMs For the Rest of Us // Vikram Sreekanti & Joseph Gonzalez // LLMs in Prod Conference Part 2.

Themes of Building LLM-based Applications for Production: A Practitioner's View LLMs For the Rest of Us // Vikram Sreekanti & Joseph Gonzalez // LLMs in Prod Conference Part 2

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:23.426158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.