arxiv: 2302.11382 · v1 · submitted 2023-02-21 · 💻 cs.SE · cs.AI

Recognition: 2 theorem links

· Lean Theorem

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Jules White , Quchen Fu , Sam Hays , Michael Sandborn , Carlos Olea , Henry Gilbert , Ashraf Elnashar , Jesse Spencer-Smith

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Douglas C. Schmidt

Authors on Pith no claims yet

Pith reviewed 2026-05-15 07:04 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords prompt engineeringprompt patternslarge language modelsChatGPTsoftware patternsLLM interactionsprompt catalog

0 comments

The pith

A catalog of prompt patterns provides reusable solutions to common problems in LLM conversations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper offers a framework for documenting prompt engineering techniques as patterns. It presents a catalog of patterns that have successfully improved outputs from models like ChatGPT. These patterns help enforce rules and automate processes in prompt design. The work also shows how to combine patterns for more sophisticated interactions. This approach treats prompts as a form of programming for customizing LLM behavior.

Core claim

The central claim is that prompt patterns act as reusable solutions to common problems in output generation and interaction with large language models, allowing for systematic improvement in prompt engineering through a documented catalog that can be adapted across domains.

What carries the argument

The prompt pattern, defined as a structured template providing reusable solutions to problems in LLM prompting, analogous to software design patterns.

If this is right

Prompts can be constructed by combining multiple patterns to handle complex tasks.
The catalog enables knowledge transfer of effective prompting strategies.
Patterns support automation of software development tasks using LLMs.
Outputs gain specific qualities and quantities as enforced by the patterns.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Adopting these patterns could standardize prompt engineering practices across teams.
Future tools might automatically suggest or generate prompts based on the catalog.
The framework could extend to other AI systems beyond language models.
Testing the patterns on emerging LLMs would validate their broad applicability.

Load-bearing premise

The documented patterns will transfer effectively to new domains, tasks, and different large language models.

What would settle it

A controlled experiment showing that prompts built with the catalog produce no better results than ad-hoc prompts on a new set of tasks would falsify the claim.

read the original abstract

Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a particular context, i.e., output generation and interaction when working with LLMs. This paper provides the following contributions to research on prompt engineering that apply LLMs to automate software development tasks. First, it provides a framework for documenting patterns for structuring prompts to solve a range of problems so that they can be adapted to different domains. Second, it presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. Third, it explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A practical catalog of prompt patterns for ChatGPT that organizes techniques like software design patterns, but rests success claims only on author examples without metrics or baselines.

read the letter

This paper's core offering is a catalog of reusable prompt patterns for LLMs, framed explicitly as an analog to software design patterns. The authors document a framework for describing these patterns so they transfer across domains and show how to combine them for tasks like code generation or requirements clarification. That structure is the main new piece here, and it gives practitioners a ready set of templates rather than scattered tips. The examples feel concrete and tied to software engineering workflows, which makes the work immediately usable for people already prompting LLMs in development settings. The documentation approach itself is a small but clear step forward for turning ad-hoc prompting into something more systematic. The main limitation is the evidence base. Claims that the patterns have been applied successfully come from the authors' own constructed demonstrations, with no A/B comparisons, user studies, accuracy measures, or baseline results reported. Readers are left to verify the improvements themselves. That keeps the contribution descriptive and practical rather than rigorously tested. The paper targets software engineers and prompt engineers who need quick, adaptable techniques for LLM-assisted automation. It will be most valuable to practitioners who want templates they can try immediately, and less so to researchers looking for quantified gains or formal analysis. I would bring it to a reading group to test a few patterns ourselves and discuss how the catalog could be extended. It deserves peer review because the organization fills a real gap in early prompt engineering work, even if referees will likely ask for more empirical grounding.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a catalog of prompt patterns for enhancing interactions with LLMs like ChatGPT. It outlines a framework for documenting these patterns, provides examples of individual patterns and their combinations, and positions them as reusable solutions analogous to software design patterns for common problems in prompt engineering.

Significance. If the patterns prove effective, the work could provide a practical knowledge transfer mechanism for prompt engineering, helping developers and users structure prompts more systematically. The analogy to software patterns is apt and the framework for documentation is a useful contribution, though the absence of rigorous evaluation metrics means the significance is primarily in organization and illustration rather than proven efficacy.

major comments (2)

[Abstract] Abstract: The claim that the patterns 'have been applied successfully to improve the outputs of LLM conversations' is not backed by quantitative validation, error measures, or comparative baselines; the support consists solely of illustrative examples constructed by the authors.
[Contributions] Contributions: The second listed contribution asserts a catalog of successfully applied patterns, but the manuscript does not specify the criteria or evidence used to determine success, which directly affects the generalizability of the framework to different domains and LLMs.

minor comments (2)

[Introduction] Introduction: Consider adding more references to existing prompt engineering literature to better contextualize the novelty of the pattern catalog relative to prior work.
[Pattern catalog sections] Pattern descriptions: Some individual pattern sections could benefit from explicit discussion of potential limitations or edge cases where the pattern may not improve outputs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments and recommendation for minor revision. We agree that the claims about successful application require clarification to accurately reflect the illustrative nature of the examples and the scope of the contribution as a framework and catalog.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that the patterns 'have been applied successfully to improve the outputs of LLM conversations' is not backed by quantitative validation, error measures, or comparative baselines; the support consists solely of illustrative examples constructed by the authors.

Authors: We agree that the wording in the abstract could imply empirical validation beyond what is provided. The paper positions the patterns as reusable solutions analogous to software design patterns, where contributions typically begin with illustrative examples. In the revised manuscript, we will update the abstract to state that the patterns 'are illustrated through examples demonstrating their use in improving LLM conversation outputs,' thereby removing any implication of quantitative success and aligning the text with the paper's focus on organization and illustration. revision: yes
Referee: [Contributions] Contributions: The second listed contribution asserts a catalog of successfully applied patterns, but the manuscript does not specify the criteria or evidence used to determine success, which directly affects the generalizability of the framework to different domains and LLMs.

Authors: We accept this point and will revise the contributions section to explicitly state that the catalog is derived from the authors' practical experience applying the patterns to common prompt engineering tasks in software development contexts. Success is demonstrated qualitatively via the provided examples rather than formal criteria or metrics. We will also add text noting the framework's intended adaptability while acknowledging that broader empirical validation across domains and LLMs remains future work. revision: yes

Circularity Check

0 steps flagged

No significant circularity: descriptive catalog without derivations or self-referential reductions

full rationale

The paper is a descriptive catalog of prompt patterns for LLM interactions, providing a documentation framework and illustrative examples of patterns and their combinations. It contains no equations, fitted parameters, predictions, or derivation chains. Claims of successful application rest on author-constructed examples rather than quantitative metrics, but this is not circularity per the rules, as no step reduces by construction to its own inputs via self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. The work is self-contained as a knowledge-transfer contribution analogous to software patterns, with no ansatzes smuggled via citation or uniqueness theorems imported from prior author work that would force the result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on the domain assumption that software engineering patterns translate directly to prompt engineering for LLMs, with no free parameters, invented entities, or additional axioms stated.

axioms (1)

domain assumption Prompt patterns provide reusable solutions to common problems in LLM output generation and interaction
Invoked in the abstract as the basis for the catalog and framework.

pith-pipeline@v0.9.0 · 5527 in / 984 out tokens · 47662 ms · 2026-05-15T07:04:31.897769+00:00 · methodology

discussion (0)

Forward citations

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