- Optimising Responses : Makes the most of language models by specifically guiding the AI to obtain more precise, relevant and useful answers.
- Adaptability : Helps personalise responses according to the specific needs of the user or application context.
- Error Prevention : Reduces the risk of inaccurate or inappropriate responses by making expectations clear.
- Precise wording: Formulate clear and specific prompts to obtain more targeted results.
- Structuring the Prompt : Use a prompt structure that effectively guides the AI, such as questions, step-by-step instructions, or hypothetical scenarios.
- Iterative Feedback : Adapt and refine the prompts according to the responses obtained to gradually improve the quality of the results.
- Using Contextual Models : Integrate relevant contextual information into the prompt for better understanding by the AI.
Content creation
or generating creative texts, articles, poems, etc.
Virtual Assistance
Improve interactions with chatbots and virtual assistants.
Education and Training
Used in educational tools to provide personalised explanations or answers to specific questions.
Complexity Variable
The quality of responses depends very much on how the prompt is formulated.
Model Limits
Language models have their own limitations and cannot always correctly interpret prompts that are poorly formulated or too vague.
Ethics and Responsibility
Need to formulate prompts that avoid bias and misinformation and comply with ethical standards.
Adoption of AI in companies
According to various industry reports, a significant proportion of businesses around the world have begun to integrate AI into their operations. For example, a report by McKinsey Global Survey on AI reported that more than half of the companies surveyed had adopted AI in at least one business function.
Growth of the AI market :
The global AI market is growing rapidly. According to Statistathe global AI market was estimated to be worth tens of billions of dollars, and is set to continue growing at a considerable annual rate.
Prompt engineering" consists of meticulously formulating instructions or queries for artificial intelligence systems, in order to obtain specific, precise and relevant results, taking advantage of their ability to interpret and react to detailed and well-structured instructions.