Paper Citation Record · LEDGER

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity

As of 19 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2507.18638.

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

pith.paper-citation-record.v1
2507.18638 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:37:38.459837Z

measured 14 of 14 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T05:28:43.607900Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T05:33:04.235308Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy10
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ce6824e6-2e90-4778-bb9b-24498edbdfb2 · outbound

This paper cites A Practical Survey on Zero-shot Prompt Design for In-context Learning.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity A Practical Survey on Zero-shot Prompt Design for In-context Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:37:38.408250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc575519-9761-48a0-b0c1-98d1710de879 · outbound

This paper cites Fairness-guided Few-shot Prompting for Large Language Models,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Fairness-guided Few-shot Prompting for Large Language Models,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.638589Z

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 084eb0fa-6c82-4ca6-8513-85564fa177d8 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T22:37:38.417827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3d86b0d-47fc-4c3d-a619-d0a125fb0127 · outbound

This paper cites Guidelines for Prompting Large Language Models,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Guidelines for Prompting Large Language Models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.627392Z

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 e099cd82-4177-4413-b3bd-7dba859edb2d · outbound

This paper cites Assigning Roles to Chatbots,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Assigning Roles to Chatbots,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.615900Z

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 8baa7051-c0af-4216-8825-a5057b9562bb · outbound

This paper cites Automatic Prompt Engineer (APE),.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Automatic Prompt Engineer (APE),

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.604170Z

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 1808ae3a-a411-4c86-9005-0f9436fc2433 · outbound

This paper cites Prompt Tuning, Hard Prompts and Soft Prompts,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Prompt Tuning, Hard Prompts and Soft Prompts,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.591648Z

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 a1776c99-a409-44c8-b2ba-b21d457fa3f9 · outbound

This paper cites RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.578612Z

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 3cddf09b-d4f8-4c9d-85b3-669bbd6404fa · outbound

This paper cites Automatic Prompt Optimization with "Gradient Descent" and Beam Search.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Automatic Prompt Optimization with "Gradient Descent" and Beam Search

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:37:38.443498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81904bcf-8c78-451b-915d-296a2d6892af · outbound

This paper cites Enhancing English Comprehension through Generative AI and Prompt Engineering: A Study on Undergraduate Learning Outcomes,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Enhancing English Comprehension through Generative AI and Prompt Engineering: A Study on Undergraduate Learning Outcomes,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.566130Z

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 8a20c93b-cd59-4548-b027-3bb829fb2d7a · outbound

This paper cites Mastering generative AI: Why effective prompting is the key to success at work,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity Mastering generative AI: Why effective prompting is the key to success at work,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.553793Z

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 b3863b71-0c2e-4aff-94ee-8bae0be4ab72 · outbound

This paper cites The Critical Role of Prompt Engineering in the Workplace,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity The Critical Role of Prompt Engineering in the Workplace,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.541468Z

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 c8ac6f4d-23c8-4cba-9496-0c56036beaf9 · outbound

This paper cites The Cognitive Effects of AI-Human Collaboration: A Behavioral Study on Prompt Design and Decision-Making,.

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity The Cognitive Effects of AI-Human Collaboration: A Behavioral Study on Prompt Design and Decision-Making,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:37:38.529467Z

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

Observation f5927dc0-94d9-4b2e-b423-ff2f6d7ae65c · inbound

Less Back-and-Forth: A Comparative Study of Structured Prompting cites this paper.

Less Back-and-Forth: A Comparative Study of Structured Prompting Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:33:04.238199Z

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.