Generative artificial intelligence is transforming the way businesses produce content, automate tasks, and make decisions. However, AI is not magic: its results depend directly on the quality of the instructions you give him.
That's the whole point of Prompt Engineering : the art of formulating queries that allow AI to generate relevant, accurate answers that are adapted to your needs.
Chez Almera, we help businesses and individuals to train in these techniques to fully exploit the potential of AI. In this article, find out 15 advanced methods of prompt engineering with detailed explanations, practical cases and ready-to-use prompts.
The Zero-shot is the simplest form of prompt engineering: Zero-shot prompting consists in soliciting artificial intelligence without providing an example or context beforehand. The user asks his question or gives instructions directly, and the AI must rely solely on his internal knowledge to answer. This technique is particularly interesting for simple interactions or when you want to test the general ability of the model to understand a new demand, but it can generate inaccurate answers if the formulation is not clear or too broad.
➡️ This method allows for quick execution for current requests.
➡️ It's also a good option for testing AI capabilities without initial bias.
💼 Use cases: you want to get a first draft of a text, a quick summary of a document, a creative idea.
➡️ “List the 10 major e-commerce trends in 2025.”
➡️ “Explain the importance of AI for HR management in 200 words.”
One-shot prompting offers the AI an example, usually structured or contextual, to illustrate how the user expects a response. This makes it possible to set clear criteria in terms of style, format or content, while offering great adaptability. The AI uses it as a guide, which improves the relevance of the response, especially for tasks such as writing sample emails, articles, or solving problems similar to the example proposed.
➡️ This technique allows you to give a clear direction while allowing the AI to generate freely.
➡️ It comes in handy when you want to get a response in a specific tone.
💼 Use cases : you want to align a LinkedIn post or a commercial email with the tone of your company.
➡️ “Email example:
Purpose: Discover our new training
Body: Hello [First name], We are launching a new AI course...
Now write a similar email to promote our digital marketing workshop.”
With few-shot prompting, several representative examples (usually between two and five) are provided to the AI. This method makes it possible to instruct the model on variations, nuances of tone or structure that the user wishes to find in the response. The model “learns” implicitly from these examples and becomes more reliable in order to generate responses that are homogeneous, accurate and adapted to various professional contexts.
➡️ This technique allows AI to better understand your expectations.
➡️ You will get more consistent responses.
💼 Use cases :
➡️ You want to design structured and professional product sheets
➡️ You must create posts for social networks that respect an editorial charter or line.
➡️ “Here are two examples of LinkedIn posts. Write a third on the topic “how AI improves the productivity of SMEs”.
Example 1: [post]
Example 2: [post]”
Multi-shot prompting goes even further, by multiplying the examples presented to the AI. This technique aims at robustness, because it exposes AI to a wide variety of scenarios or formulations. It is particularly useful for complex problems or tasks requiring a high degree of consistency in the processing of information, such as the analysis of feelings on heterogeneous data sets.
➡️ Ideal for complex tasks where consistency is key.
➡️ Reduces the risk of tone differences, inconsistency, and lack of precision in responses
💼 Use cases :
➡️ Great for chatbots with aligned responses
➡️ This technique is also involved in the generation of standardized technical documentation.
“Here are 10 examples of product descriptions. Write a new description for this product: [product information].”
The iterative approach involves unfolding the conversation over several exchanges: after an initial response, the user adds details or reformulates the request to gradually refine the result. This process makes it possible to converge on more targeted responses, as each interaction builds on the context and the previous response, capitalizing on continuous improvement of the prompt.
➡️ This technique allows the result to be gradually refined by offering a clear structure.
➡️ Allows you to correct or add details to improve accuracy.
💼 Use cases :
➡️ This technique is used in the writing of blog articles.
➡️ Also useful in building a training plan.
Chain-of-thought prompting encourages AI to explain its reasoning step by step. Rather than providing a raw response, the model details all the intermediate arguments that lead to the final result. This progressive explanation is valuable for understanding how AI gets its conclusions, and often makes it possible to identify and correct logical errors, which is ideal for complex calculations or problems requiring a logical sequence.
➡️ This technique improves the logic of the AI and offers you a correctly articulated answer.
➡️ It is useful for calculations, analyses and comparisons.
💼 Use cases :
➡️ It makes it possible to improve strategic decision making.
➡️ As well as the resolution of complex problems.
“Explain your reasoning step by step to compare the pros and cons of investing in AI in an SME.
Tree-of-thought prompting requires the AI to explore several alternative ways of thinking about the same problem. Each branch of the “tree” represents a potential thought path leading to different solutions. This technique is particularly suitable for situations where there is no single solution, but several possible strategies, such as during advanced brainstorming or the resolution of open problems.
➡️ Tree-of-Thought prompting stimulates creativity.
➡️ This technique makes it possible to compare alternatives and thus obtain an answer that best meets expectations.
💼 Use cases :
➡️ In the case of a desire for Product Innovation.
➡️ Or in the development of new marketing strategies.
“Propose three different approaches to improve customer loyalty, analyze each approach, then choose the most suitable one for an SME.”
The self-consistency method consists in having the AI generate several responses for the same instruction, then in comparing these responses in order to identify the most coherent or the most relevant. This technique increases reliability, because it makes it possible to smooth out “aberrant” answers and to favor majority solutions, making the output overall more credible and stable, especially in high-stakes contexts.
➡️ Reduces the risk of errors
➡️ Improves the reliability and relevance of the response obtained.
💼 Use cases :
➡️ To generate multiple article introduction proposals.
➡️ Create different creative proposals and select the best one.
“Give me three different suggested titles for a blog post about generative AI.”
The RAG approach combines the generative capabilities of AI with external information retrieval. The model is enriched by documents or knowledge bases that it can access in real time, thus ensuring more documented, up-to-date and factual answers. This technique is used, for example, to generate sector summaries, market analyses, or FAQs from validated sources
➡️ It guarantees up-to-date and potentially sourced information.
➡️ Also allows for accurate and verifiable answers.
💼 Use cases :
➡️ Customer support chatbots.
➡️ Internal assistants based on your documents.
“From our product database, answer this customer question: “What is the difference between version X and version Y?” ”
The reflexive prompt invites the AI to analyze the quality of the prompt that the user has just formulated, to criticize it, and then to propose or apply improvements. This approach promotes the continuous optimization of instructions, going beyond simple form corrections: AI can suggest specifying the request, better framing the context or adjusting the expected level of detail. It is a powerful self-improvement tool for rigorously developing effective prompts
➡️ Reflective prompts ensure continuous learning.
➡️ Also guarantee better control of the formulation.
💼 Use cases :
➡️ If you want to form a team to write effective prompts.
➡️ And subsequently, optimize performance over time.
“Here's my prompt: “Write a LinkedIn post about AI.” Improve it to be clearer and more accurate.”
In reverse prompting, you submit a response (or a result) to the AI and ask it to guess the initial prompt that generated it. This method is valuable for understanding the origin of certain outputs, auditing automated processes or improving the transparency of the functioning of the model. It also serves as a teaching tool, allowing teams to progressively improve the formulation of their instructions.
➡️ Understand how content was generated.
➡️ Discover effective formulations.
💼 Use cases :
➡️ Analysis of the prompts or data used by your competitors.
➡️ For training and pedagogy.
“Here is an AI-generated response: [text]. Propose the probable prompt that allowed this result to be obtained.”
The decomposition of tasks consists in dividing a complex request into several specific sub-tasks, easily understandable by the AI. This fragmentation reduces the risk of model error or confusion, as each prompt becomes more focused and less ambiguous. This is the preferred approach for multidisciplinary projects or analyses requiring the intervention of different types of expertise.
➡️ Breaking down tasks reduces cognitive load for AI.
➡️ And thus makes it possible to obtain more accurate and detailed results.
💼 Use cases :
➡️ For the creation of a practical guide.
➡️ For the development of a new strategy in several stages.
Extensive contextualization aims to enrich the prompt with as much relevant information as possible about the context, the target, the objectives and any specific constraints to be taken into account. The more contextualized a prompt is, the more the AI will be able to provide a relevant response adjusted to the operational reality of the company, and the more we limit the risk of generating generic or inadequate responses.
➡️ This method helps to avoid vague answers.
➡️ Generate content that can be directly used.
💼 Use cases :
➡️ In the case of writing a medium, a text or other material for the marketing department.
➡️ To prepare speeches, spitchs or meetings.
“Write an 800-word blog post on AI applied to the banking sector, aimed at innovation managers, with a professional but accessible tone.”
This approach consists in positioning the AI in the shoes of an expert or a specific character to influence the nature of the response. For example, ask, “Act like an experienced product manager in the pharmaceutical industry.” The specified role leads the AI to adopt an appropriate tone, vocabulary and analysis angles, making the answer much more relevant for targeted professional applications.
➡️ Allows you to obtain an answer written in an expert tone and makes the information credible.
➡️ This simulates a specific profile (coach, consultant, trainer...).
💼 Use cases :
➡️ For an HR coaching need.
➡️ To improve or get advice on sales strategies.
“Act like a digital strategy consultant and come up with a marketing plan for a B2B startup.”
Building a battery of tests to compare different prompts makes it possible to objectify the effectiveness of each approach. By systematically measuring the impact on the quality, relevance or speed of results, the team can adopt a pragmatic approach to continuous improvement. This agile methodology, based on A/B testing, provides an empirical basis for validating or rejecting certain prompts according to the company's business objectives.
➡️ Improves long-term performance.
➡️ Standardize the use of AI.
💼 Use cases :
➡️ To compare multiple formulations.
➡️ Allows you to build an internal library of effective and reusable prompts.
“Compare the following three versions of Prompt according to the criteria: clarity, precision, relevance.”
Prompt engineering is not just a “trick and trick”: it's a strategic discipline which makes it possible to transform AI into a real ally. The 15 techniques presented offer a solid framework for fully exploiting the power of generative models.
👉 At Almera, we support managers, marketers and operational teams to master these practices to Turning AI into a competitive advantage.
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