Artificial Intelligence

Prompt engineering

Prompt engineering is a new field of artificial intelligence that focuses on creating and optimising prompts for the efficient usage of language models (LMs) in a variety of applications and research issues. It entails fine-tuning large language models (LLMs) with specific prompts and suggested outputs, as well as fine-tuning input to various generative AI services that generate text or graphics.

Prompt engineering will be critical in generating many sorts of content, such as robotic process automation bots, 3D assets, scripts, robot instructions, and other digital artefacts, as generative AI techniques advance. Crafting precise and context-specific instructions or queries to elicit desired replies from language models, offering guidance to the model, and controlling its behaviour and output, is what prompt engineering entails.

Prompt engineering is a component of generative artificial intelligence (AI), which is revolutionising how we interact with technology. It entails the methodical creation, development, and optimisation of prompts as well as understanding of the underlying Generative Artificial Intelligence System, leading AI systems towards specified outputs and promoting effective human-AI engagement. Prompt engineering is essential for keeping an up-to-date prompt library and fostering efficiency, accuracy, and knowledge exchange among AI practitioners.

Source: YouTube

What is prompt engineering?

Prompt engineering refers to the process of crafting well-structured and contextually relevant input queries for language models to generate accurate and desired outputs. It involves carefully designing prompts to obtain specific responses.

Why is prompt engineering important?

Prompt engineering is crucial because it determines how well a language model like GPT-3.5 understands and responds to input queries. Effective prompts can lead to more accurate, relevant, and coherent outputs, enhancing the usability and reliability of the model.

What are some tips for crafting effective prompts?

Some tips include being clear and specific, providing contextual information, using variations in phrasing, specifying desired response formats, utilizing examples, and structuring prompts for complex questions.

How can I ensure accuracy in generated responses through prompt engineering?

To ensure accuracy, carefully review and test the model's generated responses with different prompts. Iterate and refine your prompts to align with accurate and factual information.

Can prompt engineering help control the output of language models?

Yes, prompt engineering can provide some level of control over the outputs by using explicit instructions or context-setting statements at the beginning of the prompt. This helps guide the model's behavior towards desired outcomes.

Should I use open-ended or closed-ended prompts for FAQ-type queries?

For FAQ-type queries, closed-ended prompts are often more effective as they guide the model to produce concise and accurate responses. Open-ended prompts might lead to creative but potentially irrelevant or lengthy answers.

How do I handle biases in generated responses using prompt engineering?

To address biases, carefully design prompts to be neutral and unbiased. Review the generated responses for any bias or controversial content and iterate on your prompts as needed.

How do I adapt my prompts to model updates?

Model updates can impact how a language model responds to prompts. Stay updated on the model's capabilities, test new prompts with updated models, and adjust your prompts accordingly to achieve desired outcomes.

Snippet from Wikipedia: Prompt engineering

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.

  • Prompt Engineering synonyms:
    • Query Crafting
    • Instruction Design
    • Input Formulation
    • Phrase Construction
    • Prompt Crafting
    • Interrogation Formulation
    • Contextual Questioning
    • Query Optimization
    • Statement Tailoring
    • Guided Interaction Design
    • Dialogue Modeling
    • Output Control Strategy
    • Response Elicitation
    • Request Design
    • Language Guiding
    • Prompt Refinement
    • Conversation Shaping
    • Structured Input Design
    • Response Steering
    • Controlled Generation Approach
Prompt Engineering Roadmap
Language Models

Understand the basics of language models, their architecture, and use cases.

Prompt Types

Study single-turn and multi-turn prompts, and their applications.

  • Instructional Prompts
  • Socratic Prompts
  • Priming Prompts
  • Mixed Prompts
  • Example-Based Prompts
  • Single-Sentence Prompts
  • Open-Ended Prompts
  • Closed-Ended Prompts
  • Contextual Prompts
  • System-Level Instructions
  • Fill-in-the-Blank Prompts
  • Comparison Prompts
  • Analogy Prompts
  • Multi-Turn Conversations
  • Code Generation Prompts
  • Summarization Prompts
  • Explanation Prompts
  • Storytelling Prompts
  • Domain-Specific Prompts
  • Negation or Reverse Prompts
  • Clarification Prompts
  • Evaluation Prompts
  • Conditional Prompts
  • Translation or Language Conversion
  • Imitation Prompts
Effective Prompts

Learn to craft clear and context-rich prompts for accurate responses.

Prompting Techniques:

  • Role Prompting
  • Few Shot Prompting
  • Chain of Thought
  • Zero Shot Chain of Thought
  • Least to Most Prompting
  • Dual Prompt Approach
  • Combining Techniques
  • Direct Question Prompting
  • Instructional Prompts
  • Contextual Prompts
  • System-Level Instructions
  • Completion Prompts
  • Comparison and Analogy Prompts
  • Storytelling Prompts
  • Summarization Prompts
  • Code Generation Prompts
  • Exploration Prompts
  • Clarification Prompts
  • Conditional Prompts
  • Opinion or Evaluation Prompts
  • Translation and Language Conversion
  • Imitation or Emulation
  • Question Chain Prompts
  • Interpretation Prompts
  • Problem-Solving Prompts
  • Reverse or Negation Prompts
  • Socratic Questioning

Practice adding context-setting instructions for specific outputs.

  • Contextual Question Prompts
  • Background Information Integration
  • Scenario-Based Prompts
  • Historical Contextualization
  • Past Interaction References
  • Progressive Context Addition
  • Sequential Conversation Simulation
  • Domain-Specific Contextualization
  • Comparative Context Application
  • Contextual Clarification Seeking
  • Real-World Application Illustration
  • Case Study Contextualization
  • Temporal Context Incorporation
  • Contextual Condition Setting
  • Multimodal Context Enrichment
  • Interactive Contextual Probing
  • Emotionally Charged Contextualization
  • Contextual Change Scenarios
  • Cultural or Geographic Contextualization
  • Contextual Constraints Introduction

Evaluate generated responses for relevance, accuracy, and biases.

  • Benchmark Testing
  • Human Evaluation
  • Accuracy Assessment
  • Contextual Relevance Analysis
  • Bias Detection and Mitigation
  • Response Consistency Check
  • Abstraction and Specificity Analysis
  • Diverse Input Variation Testing
  • Scenario Simulation Validation
  • Domain Expert Review
  • User Feedback Analysis
  • Generalization Test Cases
  • Limitation Exploration
  • Adversarial Testing
  • Intent Misinterpretation Detection
  • Evaluation Metrics Comparison
  • Ethical Implications Review
  • Sensitivity to Instruction Tweaks
  • Model Behavior Profiling
  • Feedback Loop Implementation
Refine and Optimize

More Roadmaps:

External links:

## ToDo ##

  • method/prompt_engineering.txt
  • Last modified: 2023/08/18 07:05
  • by Henrik Yllemo