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Paper Citation Record · LEDGER

Prompt Design and Engineering: Introduction and Advanced Methods

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2401.14423.

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

pith.paper-citation-record.v1
2401.14423 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:27:08.681795Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:58:45.254056Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0d3d5f23-2cb4-44b1-b9ba-4e1f95c6be88 · inbound

Ratas framework: A comprehensive genai-based approach to rubric-based marking of real-world textual exams cites this paper.

Ratas framework: A comprehensive genai-based approach to rubric-based marking of real-world textual exams Prompt Design and Engineering: Introduction and Advanced Methods

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-07T13:27:08.681795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:27:08.681795Z digest=sha256:f511bc07da61cdc9caa5a146dcb4c48cc4b62f9d60c614dc838d39415aefff7c

Observation f41b0c77-ee73-4d19-a494-2bdf36fd0cc1 · inbound

Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration cites this paper.

Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration Prompt Design and Engineering: Introduction and Advanced Methods

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-07T11:41:21.355981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:21.355981Z digest=sha256:a0e972723e4b75c1e7eb62e74653fa107fa5622520cef3661fa90d4dad912045

Observation 1e8f3f06-e12f-41a1-a718-baf3db245fc4 · inbound

A Short Survey on Formalising Software Requirements using Large Language Models cites this paper.

A Short Survey on Formalising Software Requirements using Large Language Models Prompt Design and Engineering: Introduction and Advanced Methods

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T01:07:22.850646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:07:22.850646Z digest=sha256:af7c4667977cecf981614b40ccf2fca8b13b4c1b995006ba5058054691294f72

Observation 3a763cb5-698f-4e07-9ca1-c583bf15f0d4 · inbound

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques cites this paper.

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques Prompt Design and Engineering: Introduction and Advanced Methods

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T19:37:18.348999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:37:18.348999Z digest=sha256:d45a398c7769e68b9191c5bf3576b8c46caa1d2192e11ad74560b6f6a0c9178d

Observation 435cf9cf-8fa5-444f-a341-7e3f3d29fe82 · inbound

A Survey of Context Engineering for Large Language Models cites this paper.

A Survey of Context Engineering for Large Language Models Prompt Design and Engineering: Introduction and Advanced Methods

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:58:45.257570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-13T20:58:45.060041Z digest=sha256:f08ebc27a9cdde67a4a7c55b8e5c898625a36f41c0c44b9aa2b497d60743947b

Observation 6b4ec868-c1a4-48f3-9c31-6a5627857548 · inbound

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents cites this paper.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Prompt Design and Engineering: Introduction and Advanced Methods

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T11:48:42.499047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:48:42.499047Z digest=sha256:31199ff6ed265515191c570986811c2f67487fb8c2040e6fc9f0c716f7cc0cbb

Observation f06c6cbc-0d83-45ed-92da-8018c126e8ee · inbound

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes cites this paper.

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes Prompt Design and Engineering: Introduction and Advanced Methods

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-04T18:28:34.717885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T18:28:34.717885Z digest=sha256:23a93a92c8e9e7814f8e99dcbbcb46dc69675e4583a911188245cec5505ea41a

Observation e122719a-d1dd-4aea-92d3-c7830710a7f2 · inbound

Large language models replicate and predict human cooperation across experiments in game theory cites this paper.

Large language models replicate and predict human cooperation across experiments in game theory Prompt Design and Engineering: Introduction and Advanced Methods

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-03T23:43:38.456599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T23:43:38.456599Z digest=sha256:7129d98f472d830198ab8329daf319796a831d888995eb1029569147fe0e459c

Observation 82080225-3285-403c-804f-040179c0974a · inbound

AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito cites this paper.

AI Agent for Reverse-Engineering Legacy Finite-Difference Code and Translating to Devito Prompt Design and Engineering: Introduction and Advanced Methods

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-03T08:03:52.107269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:03:52.107269Z digest=sha256:431d79c0cd2e14b731206cf983e46a7ba122d0fe7991a8ab4962bf81a527dc0b

Observation d76db149-e9e7-4f64-8fdd-32f729d72f9f · inbound

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System cites this paper.

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System Prompt Design and Engineering: Introduction and Advanced Methods

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-02T21:59:14.075995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T21:59:14.075995Z digest=sha256:114a84c3f7a65bea52bb1a82f912011b22e774a0bb59ccf501753c6973ce170d

Observation 7a6dfd25-c0f3-475e-8c2b-9140e1002aeb · inbound

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code cites this paper.

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code Prompt Design and Engineering: Introduction and Advanced Methods

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:56:30.173029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-07T15:35:02.934397Z digest=sha256:bfc51f65234916e0637cc8c85941d403d756563ed72fdc0c2f9a340bd106def8

Observation 39c58f75-bfaf-4926-b62a-88d0053fdbef · inbound

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code cites this paper.

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code Prompt Design and Engineering: Introduction and Advanced Methods

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:15:39.561527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-08T18:39:02.963388Z digest=sha256:b2a924f05f4a6d5bb103ff47f1760b0e23cd21b8f2d133c38d2ac26a0c2155da