Paper Citation Record · LEDGER

Prompt Design and Engineering: Introduction and Advanced Methods

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:35:02.343483Z

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 9bb8dff5-b055-44a6-8292-52894073dc38 · inbound

Leveraging Multimodal LLM for Inspirational User Interface Search cites this paper.

Leveraging Multimodal LLM for Inspirational User Interface Search Prompt Design and Engineering: Introduction and Advanced Methods

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T04:35:02.343483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc8ebb9f-7d32-4073-b1af-97dfc7d5d6cd · inbound

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation cites this paper.

Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation Prompt Design and Engineering: Introduction and Advanced Methods

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T06:02:12.774490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.

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.

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.

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.

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 2026-08-10T06:31:04.303077+00:00.

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.

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.

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.

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.

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.

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 2026-08-10T06:31:04.303077+00:00.

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 2026-08-10T06:31:04.303077+00:00.