FLUID Principles: Introducing Prompt Engineering & Design

Identify and avoid vagueness, incompleteness, and irrelevancy in prompts to improve model understanding and response relevance.

Address vagueness, incompleteness, and irrelevancy early to specify the problem with precision, completeness, and relevance. Use clear instruction keywords, subject specification, inner scope, and contextual information to guide prompts.

Sharpen prompts for prompt engineering by clarifying scope and subject. Use techniques with examples like the impact of deforestation on climate change, focusing on carbon sinks, biodiversity, and regional patterns.

Design prompts with precision, focusing on the problem principle to ensure relevance. Counter pitfalls like vagueness, incompleteness, and irrelevancy by specifying subject area, defining scope, and providing necessary context.

Embrace continuous experimentation and lifelong learning to design effective prompts, balancing innovation with proven methods, learning from mistakes, and staying informed about a model's strengths and limitations.

Explore the learner aptitude principle as a call to lifelong growth for prompt designers, and discover conceptual blending, statistic transplantation, and counterfactual prompting to foster dynamic, coherent outputs.

Use conceptual blending to transform simple prompts into creative prompts, such as a sunset symbolizing life’s end or urbanization framed as a dramatic play, to deepen creative thinking.

Apply stylistic transplantation to prompts by reframing them into genre narratives, such as a gothic story about teenagers and social media, or a fantasy tale about exercise and mental health.

Leverage counterfactual prompting to push models beyond established facts, reframing prompts into hypothetical scenarios and analyzing potential impacts on innovation, privacy, and global communication.

Discover how learner aptitude drives continuous exploration and creative thinking, while applying conceptual blending, stylistic transplantation, and counterfactual prompting to foster richer, diverse, and unbiased insights.

Explore responsible artificial intelligence guided by governance, traceability, reliability, equity, and sustainability to protect human rights, reduce biases, and address environmental impact across healthcare, social media, and automation.

Apply the uprightness usage principle, governable, traceable, reliable, equitable, and sustainable principles to auditing and testing of prompts, ensuring governance, transparency, privacy protection, and bias checks across AI systems.

Refine prompts for large language models to meet ethical considerations and privacy protection, turning poorly defined prompts into neutral, evidence-based queries about educational practices and policies.

Explore how UNESCO, IMF, and major tech leaders shape responsible AI with governance, privacy, and transparency, and learn to design prompts that uphold human values while mitigating bias and misinformation.

Explore how miscommunication, inconsistency, and pertinent deficit arise in large language model interactions and address them through effective prompt engineering.

Apply incisive instruction principles to craft specific prompts without detours or ambiguities, using concept clarification, tone specification, structure definition, format guidance, detail control, and expertise adjustment for language model interactions.

Learn to turn vague prompts into precise, beginner-friendly instructions with clear tone, structure, and call to action for accurate, well-structured responses.

Apply prompt engineering with clear, direct prompts, specific instructions, plain language, and tone to guide structure, format, detail, and expertise, reducing miscommunication in language models.

Grasp the problem in prompt engineering to avoid misaligned outputs and inefficient data handling. Align the problem context and goals with model capabilities for effective use of large language models.

Understand how not knowing the model drives poor prompt design and outdated responses. Recognize recency limits, biases, and knowledge constraints, and apply instruction to set realistic expectations for prompt engineering.

Practice diligent prompt crafting by thoroughly understanding the problem, defining metrics and indicators, and refining prompts to guide large language models toward precise, accurate summaries and outcomes.

Evaluate prompt design using both technical and non-technical approaches, automating correctness metrics and relevance via embeddings, and refine results through user feedback and A/B testing.

Explore six practical metrics for evaluating pre-trained language models, including relevance, faithfulness, toxicity, latency, correctness, and human evaluation, to enhance prompt design and model deployments.

Explore evaluating metrics for acceptable responses to productivity prompts in a tech company, highlighting relevance, faithfulness, and toxicity concerns while emphasizing modern techniques like work-life balance, collaboration, and agile methodologies.

Engage in the output inspection phase to move from metrics to qualitative analysis, identifying subtle factual errors, jargon misuse, and inappropriate tone, then refine prompts for clearer future outputs.

Examine how prompts and model outputs reveal ambivalent attachment and its long-term effects, and how the inspection phase corrects misrepresentations and guides accurate discussion of generalized anxiety disorder treatments.

Engage in iterative refinement, an ongoing dialogue with a large language model. Define the task, analyze inputs, run prompts, evaluate results, inspect outputs, and refine prompts to improve quality.

Explore how prompt designers refine prompts through an iterative process of problem understanding, experiments setup, output evaluation, and response inspection to balance linguistic accuracy, domain expertise, and contextual relevance.