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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 2026-08-07T06:34:17.273281+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=sha256:1dd8930723213ec2a1c7db64d0f149f7dab9c9de0557d7041e15a72791023f68

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=sha256:36b81279dda434def5104059abb78c3ef600d0a1d9cdfd7ae2ef57dfc1cdca44

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:babb6b7c22f82908e303a2146a116dfb236876f7f1942b24a1df2c99eb11a976

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=sha256:1218151e0f50658ab700f56c6dd84bf92de94f63a92d1c36b3f07f93256d7722

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-13T20:58:45.060041Z digest=sha256:ed18a4a96621357b5abcfa0fe6923c8aa24724d73cfe7d2792491472faf4da93

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=sha256:3ee5bc5b4db3f30e22b2c94b85cf655c9b5b4938f1b296bc288e71679a29e12c

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=sha256:5b12142616cb1dc3873be11ce7ab3eaa700be07dfd532c3dda4eb58bf79d51e5

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=sha256:901f49521118def0364d85990e8d7d7376be0be9f7acdfdf89a31553e03d3cad

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=sha256:020064e89878a658a367aba000fcf54e6011cebd7125364d2e1e706cb682a864

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=sha256:3e8515618c766682d3123052aa66c48ce9d70d474c28c127d19ddc00a9eb6cbb

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-07T15:35:02.934397Z digest=sha256:7d789b97c79540c4e0b0fd270f6826692f6e8e339d07d36a5a7f8086049f2d9a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T18:39:02.963388Z digest=sha256:f487602f8a7193826b2eb21e4ff77b02e8b966db9a198352f8f0823959a6f0a3