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Prompt engineering

Importance of Prompt Engineering

  1. Optimising Responses : Makes the most of language models by specifically guiding the AI to obtain more precise, relevant and useful answers.
  2. Adaptability : Helps personalise responses according to the specific needs of the user or application context.
  3. Error Prevention : Reduces the risk of inaccurate or inappropriate responses by making expectations clear.

Prompt Engineering techniques

  1. Precise wording: Formulate clear and specific prompts to obtain more targeted results.
  2. Structuring the Prompt : Use a prompt structure that effectively guides the AI, such as questions, step-by-step instructions, or hypothetical scenarios.
  3. Iterative Feedback : Adapt and refine the prompts according to the responses obtained to gradually improve the quality of the results.
  4. Using Contextual Models : Integrate relevant contextual information into the prompt for better understanding by the AI.

Precise guidance for AI leads to more relevant and useful responses


Prompt Engineering applications

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.

To watch out for

Challenges and Limitations

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.


AI is not just about

Industry and Manufacturing :

  • Production Process Automation AI is being used to automate manufacturing processes, improving efficiency, reducing errors and increasing production.
  • Predictive Maintenance AI analysis of machine data can predict equipment failure before it occurs, minimising downtime.
  • Supply Chain Optimisation AI helps to optimise supply chains by predicting demand, managing stocks and automating logistics.

Finance and Banking :

  • Fraud Detection AI is crucial in detecting fraudulent activity by analysing transaction patterns to identify suspicious behaviour.
  • Portfolio Management and Trading Advanced AI algorithms help to manage investment portfolios, analysing market trends to make informed decisions.
  • Customer Services AI improves the customer experience in the banking sector through virtual assistants and chatbots, providing fast, personalised assistance.

Health and Medicine :

  • Diagnosis and Treatment AI: AI is helping doctors to diagnose certain diseases, such as cancer, more accurately and more quickly, by analysing medical images with a precision that is often superior to that of humans.
  • Pharmaceutical Research AI accelerates the discovery of new drugs by analysing vast data sets to identify potentially therapeutic compounds.
  • Healthcare Management AI systems are being used to improve the management of patients and hospital resources, optimising care pathways and reducing costs.

Figures on
the use of ia.

Yes, there are a number of statistics and trends concerning the use of artificial intelligence (AI) across different sectors. In April 2023, here are some key points :

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.

0 %

of companies plan to increase or maintain their spending on AI in 2023.

0 %

annual growth expected for the AI market between 2023 and 2030


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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.