Text-to-* Model Prompt Programming Task

A Text-to-* Model Prompt Programming Task is a text-to-* prompt writing task that is a programming task that requires the creation of AI model text prompts (for a text-to-* model) to solve a prompt-based text-to-* model inference task.

• AKA: LLM Prompt Engineering, Prompt Design, Prompt Engineering Task, AI Prompt Creation.
• Context:
  • measure: improve their accuracy and relevance for a given task or application.
  • It can typically involve structured instruction creation that guides text-to-* model behavior toward specific text-to-* model output goals.
  • It can typically require understanding of both natural language nuance and text-to-* model capability to craft effective text-to-* model prompt.
  • It can typically benefit from iterative refinement process where text-to-* model prompt is repeatedly adjusted based on text-to-* model output evaluation.
  • It can typically influence text-to-* model response quality through careful selection of text-to-* model prompt format, text-to-* model prompt structure, and text-to-* model prompt content.
  • It can typically facilitate the creation of consistent text-to-* model response through text-to-* model constraint application.
  • ...
  • It can often be the process of crafting and optimizing instructions or queries given to AI models to elicit the desired response or action.
  • It can often apply a Prompt Engineering Technique.
  • It can often follow a Prompt Engineering Process, such as an iterative trial and error process.
  • It can often require domain-specific knowledge to effectively communicate text-to-* model task requirement.
  • It can often involve establishing text-to-* model persona to guide text-to-* model tone and text-to-* model expertise level.
  • It can often include context window management to ensure all relevant text-to-* model prompt information fits within text-to-* model token limit.
  • It can often incorporate text-to-* model example selection to demonstrate desired text-to-* model output pattern.
  • It can often necessitate error case anticipation to prevent common text-to-* model misunderstanding.
  • ...
  • It can range from being Text-to-Text Prompting, to being Text-to-Code Prompting, to being Text-to-Image Prompting, to being Text-to-Multi-Modal Prompting.
  • It can range from being Manual Prompt Engineering (by a prompt engineer) to being Automated Prompt Engineering (by a prompt engineering system).
  • It can range from being Zero-Shot Prompt Engineering (of zero-shot prompts) to being a Example-Including Prompt Engineering (of example-including prompts).
  • It can range from being a Declarative Text-to-* Model Prompt Programming Task to being an Imperative Text-to-* Model Prompt Programming Task, depending on its text-to-* model instruction style.
  • It can range from being a Simple Text-to-* Model Prompt Programming Task to being a Complex Text-to-* Model Prompt Programming Task, depending on its text-to-* model reasoning requirement.
  • It can range from being a Single-Turn Text-to-* Model Prompt Programming Task to being a Multi-Turn Text-to-* Model Prompt Programming Task, depending on its text-to-* model interaction pattern.
  • It can range from being a Domain-General Text-to-* Model Prompt Programming Task to being a Domain-Specific Text-to-* Model Prompt Programming Task, depending on its text-to-* model specialization level.
  • It can range from being a Human-Readable Text-to-* Model Prompt Programming Task to being a Machine-Optimized Text-to-* Model Prompt Programming Task, depending on its text-to-* model prompt design approach.
  • ...
  • It can be covered in Prompt Engineering Training Session.
  • It can involve creating prompts to extract specific information from AI models using clear and structured language.
  • It can be supported by a Text-to-* Model Prompt Programming System.
  • It can be associated with Text-to-* Model Prompt Optimization, where experimentation and refinement improve prompt efficiency.
  • It can view prompts as "natural language code," functioning as high-level abstractions to guide model behavior.
  • It can include crafting prompts with sample input-output examples to clarify model behavior.
  • It can enable Meta-Prompts where prompts create or modify other prompts for scaling tasks.
  • It can involve text-to-* model system prompt design as a foundational layer for text-to-* model user prompt interaction.
  • It can incorporate text-to-* model temperature adjustment to control text-to-* model output randomness.
  • It can utilize text-to-* model token economy strategy to maximize text-to-* model context window usage.
  • It can require text-to-* model output format specification for structured text-to-* model response formatting.
  • It can benefit from text-to-* model instruction decomposition to enhance text-to-* model task comprehension.
  • It can employ text-to-* model error handling instruction to manage text-to-* model edge case behavior.
  • It can integrate text-to-* model tool use directive to enable text-to-* model external resource interaction.
  • It can leverage text-to-* model prompt template library for consistent text-to-* model prompt pattern application.
  • ...
• Example(s):
  • Core Prompt Engineering Tasks, such as:
    • Text-to-Text Prompt Engineerings, such as:
      • Summarization Prompt Engineering for prompt content condensation.
      • Translation Prompt Engineering for prompt language conversion.
      • Creative Writing Prompt Engineering for prompt narrative generation.
      • Question-Answering Prompt Engineering for prompt information extraction.
      • Instructional Prompt Engineering for prompt procedure explanation.
    • Text-to-Code Prompt Engineerings, such as:
      • Programming Prompt Engineering for prompt code creation.
      • Debug Prompt Engineering for prompt error identification.
      • Technical Documentation Prompt Engineering for prompt instruction creation.
      • Algorithm Design Prompt Engineering for prompt solution architecture.
      • Code Refactoring Prompt Engineering for prompt code optimization.
    • Text-to-Image Prompt Engineerings, such as:
      • Art Prompt Engineering for prompt visual creation.
      • Design Prompt Engineering for prompt style specification.
      • Photorealistic Prompt Engineering for prompt reality simulation.
      • Concept Art Prompt Engineering for prompt imaginative visualization.
      • Character Design Prompt Engineering for prompt persona representation.
  • Business Prompt Engineering Tasks, such as:
    • Strategy Developments, such as:
      • Business Case Prompt Engineering for prompt analysis optimization.
      • Market Research Prompt Engineering for prompt insight generation.
      • Competitive Analysis Prompt Engineering for prompt market assessment.
      • SWOT Analysis Prompt Engineering for prompt organizational evaluation.
      • Risk Assessment Prompt Engineering for prompt uncertainty quantification.
    • Content Creations, such as:
      • Role-Based Prompt Engineering for prompt consultant simulation.
      • Memo Creation Prompt Engineering for prompt executive communication.
      • Product Ideation Prompt Engineering for prompt innovation development.
      • Marketing Copy Prompt Engineering for prompt persuasive messaging.
      • Email Template Prompt Engineering for prompt communication standardization.
  • Advanced Prompt Engineering Tasks, such as:
    • Meta Engineerings, such as:
      • AI Metaprompt Engineering for prompt generation tasks.
      • Task Decomposition Prompt Engineering for prompt subtask creation.
      • Data Analysis Prompt Engineering for prompt insight extraction.
      • Multi-Agent Prompt Engineering for prompt collaborative reasoning.
      • Tool-Using Prompt Engineering for prompt external resource utilization.
    • Optimization Engineerings, such as:
      • Iterative Prompt Engineering for prompt response improvement.
      • Context Enhancement Prompt Engineering for prompt clarity optimization.
      • Output Adaptation Prompt Engineering for prompt content refinement.
      • Token Economy Prompt Engineering for prompt efficiency maximization.
      • Error Handling Prompt Engineering for prompt robustness improvement.
  • Training Prompt Engineering Tasks, such as:
    • Educational Engineerings, such as:
      • Overview Prompt Engineering for prompt basic instruction.
      • Advanced Prompt Engineering for prompt optimization technique.
      • Workshop Prompt Engineering for prompt interactive learning.
      • Tutorial Prompt Engineering for prompt step-by-step guidance.
      • Assessment Prompt Engineering for prompt learning evaluation.
    • Performance Engineerings, such as:
      • Performance Prompt Engineering for prompt efficiency improvement.
      • Quality Assurance Prompt Engineering for prompt output verification.
      • Benchmark Prompt Engineering for prompt comparative assessment.
      • Scalability Prompt Engineering for prompt processing optimization.
      • Integration Prompt Engineering for prompt system compatibility.
  • Specialized Domain Prompt Engineering Tasks, such as:
    • Medical Prompt Engineerings, such as:
      • Diagnostic Prompt Engineering for prompt symptom analysis.
      • Treatment Recommendation Prompt Engineering for prompt therapeutic suggestion.
      • Medical Literature Review Prompt Engineering for prompt research synthesis.
    • Legal Prompt Engineerings, such as:
      • Contract Analysis Prompt Engineering for prompt agreement evaluation.
      • Legal Research Prompt Engineering for prompt precedent identification.
      • Case Summarization Prompt Engineering for prompt litigation distillation.
    • Scientific Prompt Engineerings, such as:
      • Experiment Design Prompt Engineering for prompt methodology creation.
      • Data Interpretation Prompt Engineering for prompt result analysis.
      • Research Question Prompt Engineering for prompt hypothesis formulation.
  • ...
• Counter-Example(s):
  • Feature Engineering Task, which involves manually creating input features for models rather than prompt design.
  • LLM Fine-Tuning Task, where models are retrained with new data rather than refined through prompt-based methods.
  • Software Code Programming Task, where software code is written without utilizing prompt-based input.
  • Neural Network Architecture Design, which involves structuring model components rather than crafting instructions.
  • Dataset Curation Task, which focuses on selecting training examples rather than creating inferential prompts.
  • Hyperparameter Optimization Task, which tunes model parameters rather than input instructions.
  • Model Deployment Task, which configures infrastructure rather than model interaction patterns.
  • Prompt Tuning Task, which updates continuous vector representations rather than natural language prompts.
• See: Prompt Tuning, Prompt-based Learning, Train of Thought, Few-Shot Learning, Transfer Learning, Chain of Thought, Tree of Thought, Text-to-* AI Model Prompt Development Technique, Prompt Template Library, In-Context Learning, Prompt Engineering Framework, Zero-Shot Prompting.

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References

2024

• (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Prompt_engineering Retrieved:2024-9-19.
  • Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative AI model. A prompt is natural language text describing the task that an AI should perform: a prompt for a text-to-text language model can be a query such as "what is Fermat's little theorem?", a command such as "write a poem about leaves falling", or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, providing relevant context or assigning a role to the AI such as "Act as a native French speaker". A prompt may include a few examples for a model to learn from, such as asking the model to complete "maison house, chat cat, chien " (the expected response being dog), an approach called few-shot learning. When communicating with a text-to-image or a text-to-audio model, a typical prompt is a description of a desired output such as "a high-quality photo of an astronaut riding a horse" or "Lo-fi slow BPM electro chill with organic samples". Prompting a text-to-image model may involve adding, removing, emphasizing and re-ordering words to achieve a desired subject, style, layout, lighting, and aesthetic.

2024

• https://youtu.be/T9aRN5JkmL8
  • NOTE: The interview discusses the evolving practice of prompt engineering, focusing on crafting effective instructions for AI models. Participants emphasize that prompt engineering involves clear communication, iterative experimentation, and a deep understanding of how models process input. They highlight how prompt engineering is akin to teaching, requiring anticipation of edge cases and refinement of tasks. While models are becoming better at understanding prompts, the conversation suggests that future interactions may shift toward collaborative efforts, where AI helps users clarify instructions. Overall, prompt engineering remains essential for extracting top performance, particularly in complex or high-stakes scenarios.

2023

• (Amatriain, 2024) ⇒ Xavier Amatriain. (2024). “Prompt Design and Engineering: Introduction and Advanced Methods.” In: arXiv preprint arXiv:2401.14423. doi:10.48550/arXiv.2401.14423
  • QUOTE: Prompt design and engineering has become an important discipline in just the past few months. In this paper, we provide an introduction to the main concepts and design approaches. We also provide more advanced techniques all the way to those needed to design LLM-based agents. We finish by providing a list of existing tools for prompt engineering.
  • NOTES:
    • Prompt Engineering can elaborate on advanced prompt engineering techniques such as Chain of Thought (CoT), Tree of Thought (ToT), and Retrieval Augmented Generation (RAG) to overcome inherent limitations of LLMs like transient state, probabilistic nature, and outdated information.
    • Prompt Engineering can discuss the integration of external knowledge through RAG to enrich LLM outputs, making them more informed and contextually relevant by dynamically incorporating real-time or domain-specific information.
    • Prompt Engineering can survey a variety of tools and frameworks developed to aid prompt engineers, such as Langchain, Semantic Kernel, Guidance library, Nemo Guardrails, and LlamaIndex, highlighting their contributions to streamlining prompt engineering processes.
    • Prompt Engineering can address the inherent challenges in prompt design, including the need to understand the AI model's capabilities, the context of its application, and the creative and domain knowledge required to craft effective prompts.
    • Prompt Engineering can underscore the significance of continuous innovation in prompt engineering to keep pace with the rapid evolution of LLMs and generative AI, suggesting that emerging techniques like Automatic Prompt Engineering (APE) may become standard practices.
    • Prompt Engineering can conclude by emphasizing the growing criticality of prompt design and engineering in the AI field, encouraging practitioners to engage in this burgeoning area to shape the future trajectory of LLMs and generative AI technologies.
    • Prompt Engineering is highlighted as crucial for maximizing the potential of Large Language Models (LLMs), underpinning the development of effective Artificial Intelligence applications through the craft of prompts.

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Prompt_engineering Retrieved:2023-6-17.
  • Prompt engineering is a concept in artificial intelligence, particularly natural language processing. In prompt engineering, the description of the task that the AI is supposed to accomplish is embedded in the input, e.g. as a question, instead of it being explicitly given. Prompt engineering typically works by converting one or more tasks to a prompt-based dataset and training a language model with what has been called "prompt-based learning" or just "prompt learning".

2023

• (ChatGPT-OpenAi, 2023) ⇒ https://chat.openai.com Retrieved:2023-4-23.
  • Q: What is Text-to-* Model Prompt Engineering?
  • A: Text-to-* model prompt engineering refers to the process of designing and refining prompts or inputs for text-based models such as natural language processing (NLP) models, text classification models, or text generation models. The goal of prompt engineering is to improve the performance of these models by providing them with more effective inputs or prompts.

    In text-to-* models, the prompt is the initial text or query that is given to the model, which it uses to generate a response or output. The quality and specificity of the prompt can have a significant impact on the accuracy and relevance of the model's output.

    Prompt engineering involves several steps, including:

    • 1. Data preprocessing: This involves cleaning and organizing the data that will be used to create the prompts.
    • 2. Prompt selection: This involves choosing the most appropriate prompt or query for the given task or application. This may involve analyzing the input data and selecting prompts that are most likely to elicit the desired response from the model.
    • 3. Prompt refinement: This involves iteratively refining the prompt or query to improve its effectiveness. This may involve adjusting the wording, structure, or format of the prompt based on the model's performance.
    • 4. Evaluation: This involves testing the performance of the model using the refined prompt and making further adjustments as needed.

Overall, prompt engineering is a key component of developing and optimizing text-to-* models, as it can help to improve their accuracy, relevance, and effectiveness for a given task or application.

2023b

• (ChatGPT-OpenAi, 2023) ⇒ https://chat.openai.com
  • ... Another term that more specifically reflects the AI and NLP-focused nature of prompt engineering is “prompt programming". Prompt programming refers to the process of creating prompts or queries that are used to elicit specific responses from NLP models. The term "programming" emphasizes the technical nature of the task and suggests a more structured approach to designing prompts tailored to the needs of specific NLP models. ...

2023c

• (Liu et al., 2023) ⇒ Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. (2023). “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing.” In: ACM Computing Surveys, 55(9).
  • QUOTE: ... Now, as of this writing in 2021, we are in the middle of a second sea change, in which the “pre-train, fine-tune” procedure is replaced by one in which we dub “pre-train, prompt, and predict.” In this paradigm, instead of adapting pre-trained LMs to downstream tasks via objective engineering, downstream tasks are reformulated to look more like those solved during the original LM training with the help of a textual prompt. For example, when recognizing the emotion of a social media post, “I missed the bus today,” we may continue with a prompt “I felt so ” and ask the LM to fill the blank with an emotion-bearing word. Or if we choose the prompt “English: I missed the bus today. French: ”), then an LM may be able to fill in the blank with a French translation. In this way, by selecting the appropriate prompts we can manipulate the model behavior so that the pre-trained LM itself can be used to predict the desired output, sometimes even without any additional task-specific training (Table 1(d); e.g., Brown et al. [9], Petroni et al. [100], Radford et al. [105], Schick and Schütze [120]). The advantage of this method is that, given a suite of appropriate prompts, a single LM trained in an entirely unsupervised fashion can be used to solve a great number of tasks [9, 131]. However, as with most conceptually enticing prospects, there is a catch — this method introduces the necessity for prompt engineering, finding the most appropriate prompt to allow a LM to solve the task at hand. ...

2023d

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/prompt_engineering Retrieved:2023-2-26.
  • Prompt engineering is a concept in artificial intelligence, particularly natural language processing (NLP). In prompt engineering, the description of the task is embedded in the input, e.g., as a question instead of it being implicitly given. ...

2022

• (Zhou et al., 2022) ⇒ Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. (2022). “Learning to Prompt for Vision-language Models.” International Journal of Computer Vision 130, no. 9
  • ABSTRACT: Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different from the traditional representation learning that is based mostly on discretized labels, vision-language pre-training aligns images and texts in a common feature space, which allows zero-shot transfer to a downstream task via prompting, i.e., classification weights are synthesized from natural language describing classes of interest. In this work, we show that a major challenge for deploying such models in practice is prompt engineering, which requires domain expertise and is extremely time-consuming—one needs to spend a significant amount of time on words tuning since a slight change in wording could have a huge impact on performance. Inspired by recent advances in prompt learning research in natural language processing (NLP), we propose Context Optimization (CoOp), a simple approach specifically for adapting CLIP-like vision-language models for downstream image recognition. Concretely, CoOp models a prompt’s context words with learnable vectors while the entire pre-trained parameters are kept fixed. To handle different image recognition tasks, we provide two implementations of CoOp: unified context and class-specific context. Through extensive experiments on 11 datasets, we demonstrate that CoOp requires as few as one or two shots to beat hand-crafted prompts with a decent margin and is able to gain significant improvements over prompt engineering with more shots, e.g., with 16 shots the average gain is around 15% (with the highest reaching over 45%). Despite being a learning-based approach, CoOp achieves superb domain generalization performance compared with the zero-shot model using hand-crafted prompts.

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