The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure

• URL: http://arxiv.org/abs/2604.14197v1
• Date: Fri, 03 Apr 2026 03:06:03 GMT
• Title: The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure
• Abstract summary: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently.<n>This paper presents PICCO, a framework derived through a rigorous synthesis of 11 previously published prompting frameworks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently. Our purpose was to derive a reference framework for structuring LLM prompts. This paper presents PICCO, a framework derived through a rigorous synthesis of 11 previously published prompting frameworks identified through a multi-database search. The analysis yields two main contributions. First, it proposes a taxonomy that distinguishes prompt frameworks, prompt elements, prompt generation, prompting techniques, and prompt engineering as related but non-equivalent concepts. Second, it derives a five-element reference architecture for prompt generation: Persona, Instructions, Context, Constraints, and Output (PICCO). For each element, we define its function, scope, and relationship to other elements, with the goal of improving conceptual clarity and supporting more systematic prompt design. Finally, to support application of the framework, we outline key concepts relevant to implementation, including prompting techniques (e.g., zero-shot, few-shot, chain-of-thought, ensembling, decomposition, and self-critique, with selected variants), human and automated approaches to iterative prompt engineering, responsible prompting considerations such as security, privacy, bias, and trust, and priorities for future research. This work is a conceptual and methodological contribution: it formalizes a common structure for prompt specification and comparison, but does not claim empirical validation of PICCO as an optimization method.

Related papers

• ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval [62.80735412748694]
  ConceptFormer is a latent concept representation learning framework for visual document retrieval.<n>It query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance.<n>It achieves 16.7% and 22.1% relative improvements in average NDCG@10 over the strongest visual retrieval baseline and the strongest OCR-based text retrieval baseline, respectively.
  arXiv  Detail & Related papers  (2026-08-16T12:07:12Z)
• Improving Scientific Document Retrieval with Academic Concept Index [47.95234352955763]
  Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations.<n>Recent approaches address these issues through two independent directions.<n>We introduce an academic concept index, which extracts key concepts from papers and organizes them guided by an academic taxonomy.
  arXiv  Detail & Related papers  (2026-01-02T04:47:49Z)
• Improving Alignment Between Human and Machine Codes: An Empirical Assessment of Prompt Engineering for Construct Identification in Psychology [0.0]
  We present an empirical framework for optimizing prompt performance for identifying constructs in texts via prompt engineering.<n>We experimentally evaluate five prompting strategies -codebook-guided empirical prompt selection, automatic prompt engineering, persona prompting, chain-of-thought reasoning, and explanatory prompting.<n>Across three constructs and two models, the classifications most aligned with expert judgments resulted from a few-shot prompt combining codebook-guided empirical prompt selection with automatic prompt engineering.
  arXiv  Detail & Related papers  (2025-12-03T14:07:42Z)
• BRIDGE: Building Representations In Domain Guided Program Verification [67.36686119518441]
  BRIDGE decomposes verification into three interconnected domains: Code, Specifications, and Proofs.<n>We show that this approach substantially improves both accuracy and efficiency beyond standard error feedback methods.
  arXiv  Detail & Related papers  (2025-11-26T06:39:19Z)
• CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension [55.29309306566238]
  Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents.<n>This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents.<n>We draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation.
  arXiv  Detail & Related papers  (2025-10-07T02:16:30Z)
• Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering [51.7493726399073]
  We present a discourse-aware hierarchical framework to enhance long document question answering.<n>The framework involves three key innovations: specialized discourse parsing for lengthy documents, LLM-based enhancement of discourse relation nodes, and structure-guided hierarchical retrieval.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)
• PromptPrism: A Linguistically-Inspired Taxonomy for Prompts [13.169345040931857]
  We introduce PromptPrism, a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels.<n>We show the practical utility of PromptPrism by applying it to three applications.
  arXiv  Detail & Related papers  (2025-05-19T01:08:26Z)
• Visual Consensus Prompting for Co-Salient Object Detection [26.820772908765083]
  We propose an interaction-effective and parameter-efficient concise architecture for the co-salient object detection task.<n>A parameter-efficient prompt tuning paradigm and seamlessly embeds consensus into the prompts to formulate task-specific Visual Consensus Prompts (VCP)<n>OurVCP outperforms 13 cutting-edge full fine-tuning models, achieving the new state of the art (with 6.8% improvement in F_m metrics on the most challenging CoCA dataset)
  arXiv  Detail & Related papers  (2025-04-19T10:12:39Z)
• Improving Subject-Driven Image Synthesis with Subject-Agnostic Guidance [62.15866177242207]
  We show that through constructing a subject-agnostic condition, one could obtain outputs consistent with both the given subject and input text prompts. Our approach is conceptually simple and requires only minimal code modifications, but leads to substantial quality improvements.
  arXiv  Detail & Related papers  (2024-05-02T15:03:41Z)
• On Meta-Prompting [17.34602431188454]
  Modern large language models (LLMs) are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them.<n>Many approaches to prompting and pre-training involve the automated generation of these prompts, also known as meta-prompting.<n>We propose a theoretical framework based on category theory to generalize and describe ICL and LLM behavior when interacting with users.
  arXiv  Detail & Related papers  (2023-12-11T17:46:44Z)
• Meta Prompting for AI Systems [12.304069891580658]
  We introduce Meta Prompting (MP), a framework that elevates the reasoning capabilities of large language models.<n>We extend MP to Recursive Meta Prompting (RMP), an automated process where an LLM can generate and refine its own prompts.<n>Our claims are validated through extensive experiments demonstrating that a Qwen-72B base model, guided by a single, example-agnostic meta-prompt, achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-20T01:51:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.