Paper Citation Record · LEDGER

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering

As of 14 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2507.03405.

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

pith.paper-citation-record.v1
2507.03405 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:14:49.378453Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T18:07:19.697951Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

23 of 23 outbound references displayed

verified exact4
verified fuzzy2
unresolved14
parse uncertain0
malformed identifier2
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c3274150-de9c-4e3a-aec2-cbd33175e7fb · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:47.800526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db85158f-9fa5-4dad-8fbf-2fcf5b7a7894 · outbound

This paper cites Optimizing Prompts for Text-to-Image Generation.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Optimizing Prompts for Text-to-Image Generation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:47.889220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28329d14-e246-4434-8e7a-3a5c630c4141 · outbound

This paper cites "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering "I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:47.998859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d161eac-a354-4c4e-8505-8a8f536db58a · outbound

This paper cites Design Guidelines for Prompt Engineering Text-to-Image Generative Models.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Design Guidelines for Prompt Engineering Text-to-Image Generative Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:48.181504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe599d96-e37f-4dcc-9d67-8f95eeeb8bd4 · outbound

This paper cites ChatGPT as a Factual Inconsistency Evaluator for Text Summarization.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering ChatGPT as a Factual Inconsistency Evaluator for Text Summarization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:48.280881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d18954c4-9e1b-49c1-97ac-a0401c06f027 · outbound

This paper cites Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:14:50.171705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2148ea12-2e1e-4402-b666-316563080f7e · outbound

This paper cites Krishna Ronanki, Beatriz Cabrero-Daniel, and Christian Berger.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Krishna Ronanki, Beatriz Cabrero-Daniel, and Christian Berger

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:48.611230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ce12b8d-d4e6-405f-ae5f-7d7ed7e466f7 · outbound

This paper cites Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

Reference 19

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:14:49.666498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77e1cfa6-e0fc-42f2-af55-58c96273aa22 · outbound

This paper cites Symbolic Knowledge Distillation: from General Language Models to Commonsense Models.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:49.093508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd258837-e2e0-4ee0-82cb-0dc57bff52d3 · outbound

This paper cites Legal Prompting: Teaching a Language Model to Think Like a Lawyer.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Legal Prompting: Teaching a Language Model to Think Like a Lawyer

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:49.296190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18c37e0f-c6f3-4bf6-bbed-59dd3d27d151 · outbound

This paper cites Large Language Models Are Human-Level Prompt Engineers.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Large Language Models Are Human-Level Prompt Engineers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:49.378453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce3a282-1eb4-49a4-a5e9-0e7b1dc323ed · outbound

This paper cites Alberto D.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Alberto D

Reference 1993

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:48.527283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b4ae13f-8573-4257-b85a-6a5aacee9fd3 · outbound

This paper cites Strandberg, Per Erik.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Strandberg, Per Erik

Reference 1997

Resolution: malformed identifier; doi_truncated, observed 2026-08-06T20:14:49.851275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 13479c2d-8a74-4b4a-aa0f-eeadac24cd73 · outbound

This paper cites Ian Sommerville and Pete Sawyer.Requirements Engineering: A Good Practice Guide.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Ian Sommerville and Pete Sawyer.Requirements Engineering: A Good Practice Guide

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:48.823152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2d11972-60f3-4251-9102-e8a79f048ac9 · outbound

This paper cites Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Humans in Humans Out: On GPT Converging Toward Common Sense in both Success and Failure

Reference 2007

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:14:50.379255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ede61d0-a5d1-445c-b3cf-2488b18a5e14 · outbound

This paper cites https://doi.org/https://doi.org/10.1016/j.infsof.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering https://doi.org/https://doi.org/10.1016/j.infsof

Reference 2011

Resolution: malformed identifier; no resolver link, observed 2026-08-06T20:14:47.717362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e2b86bc-e340-4201-8daa-f400d8989054 · outbound

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

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Prompt programming for large language models: Beyond the few-shot paradigm

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:14:50.797893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b99a9dbe-babc-49de-b320-8b90d52ed84a · outbound

This paper cites Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:14:50.657301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9f439f1-a5d5-4b63-b392-e8bdb49ad7c9 · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:49.186563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2859afa5-fd7e-4aef-9e4c-1213ccb7ecbc · outbound

This paper cites Language Models are Few-shot Learners.Advances in Neural Information Processing Systems, 33:1877–1901,.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Language Models are Few-shot Learners.Advances in Neural Information Processing Systems, 33:1877–1901,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:14:50.955141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 03f8d95b-bb29-4b23-9476-6a7cc87ef185 · outbound

This paper cites Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:14:50.529156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e1912a42-5e38-4f50-ada9-562f45fd9f43 · outbound

This paper cites Symbols of One-Loop Integrals From Mixed Tate Motives.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Symbols of One-Loop Integrals From Mixed Tate Motives

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:48.694044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b62697b1-d33d-43b7-af01-8d749ce58ed1 · outbound

This paper cites Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT.

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T20:14:47.415735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 1abde13f-48df-4bed-997c-1450ad10806a · inbound

Prompts Blend Requirements and Solutions: From Intent to Implementation cites this paper.

Prompts Blend Requirements and Solutions: From Intent to Implementation Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T18:07:19.697951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.