This framework is developed by a team of former McKinsey and Big 4 consultants. The presentation follows the headline-body-bumper slide format used by global consulting firms.
This product (Prompt Engineering Strategies) is a 26-slide PPT PowerPoint presentation slide deck (PPTX), which you can download immediately upon purchase.
"Prompt Engineering" is the art and science of writing the prompt to elicit the desirable or useful results from the Artificial Intelligence (AI) tool you are using. In Prompt Engineering, the description of the task is embedded in the input, e.g., as a question instead of it being implicitly given.
Like in most processes and systems, the quality of the inputs determines the quality of the outputs—i.e., garbage in, garbage out. With ChatGPT, other generative AI applications, and similar prompt-based AI models, the input is the prompt. Thus, it is imperative that the user write a well-crafted and articulated prompt to generate the highest quality, most desired output. Designing effective prompts increases the likelihood that the model will generate the most favorable and contextual response.
Prompt Engineering typically works by converting one or more tasks to a prompt-based dataset and training a language model with what has been called "prompt-based learning" or just "prompt learning."
This PowerPoint presentation explains 5 Prompt Engineering Strategies you can leverage to maximize the effectiveness of your prompts (when using ChatGPT and other prompt-based AI tools):
1. RTF (Role-Task-Format)
2. Chain of Thought (CoT)
3. RISEN Framework
4. RODES Framework
5. Chain of Density (CoD)
These Prompt Engineering frameworks allow for personalized and adaptive AI interactions, leading to superior Customer Experiences and increased Customer Satisfaction. Well-crafted prompts reduce the need for repetitive adjustments or manual interventions, leading to decreased operational costs.
Using standardized frameworks facilitates training and onboarding, allowing businesses to scale ChatGPT or other AI implementations smoothly across various departments or functions. Within this presentation, each Prompt Engineering Strategy is discussed in detail, including use cases and examples.
For background and context, this presentation also includes introductions to ChatGPT and Prompt Engineering. It also includes slide templates for you to use in your own business presentations.
This presentation also offers a comprehensive Slide Structure & Design Guide to enhance your business presentations. Utilize these guidelines to maintain consistency and professionalism across all your slides.
This PPT slide presents 5 distinct frameworks for Prompt Engineering, aimed at optimizing the use of ChatGPT and similar AI technologies. Each framework addresses specific business needs, enhancing the effectiveness of AI interactions.
The first framework, RTF (Role-Task-Format), emphasizes defining the role of ChatGPT, the specific task it should perform, and the desired output format. This foundational approach ensures clarity in expectations, which is crucial for effective AI utilization.
Next, the Chain of Thought (CoT) framework guides the AI through a structured reasoning process. This method enhances analytical capabilities by breaking down tasks into manageable steps, facilitating deeper insights and more accurate outputs.
The RISEN framework is tailored for specific constraints. It outlines the role, provides detailed instructions, sets an end goal, and establishes narrowing guidelines. This structured approach is particularly useful for complex tasks that require precision and clarity.
The RODES framework focuses on tasks that resemble prior examples. It details the role, states the objective, and includes specific examples along with a sense check for comprehension. This helps in ensuring that the AI aligns closely with user expectations.
Lastly, the Chain of Density (CoD) framework employs an iterative approach, utilizing recursion to refine outputs based on established benchmarks. This method allows for continuous improvement in AI performance.
The slide concludes with key takeaways, highlighting that well-crafted prompts can reduce the need for adjustments and manual interventions, ultimately lowering operational costs. Standardized frameworks also facilitate training and onboarding, enabling smoother scaling of AI implementations across departments.
This PPT slide presents an overview of "Prompt Engineering," a critical concept in artificial intelligence, particularly in natural language processing. It emphasizes the importance of crafting precise prompts to achieve desired outcomes from AI tools like ChatGPT. The slide outlines that the quality of the input directly influences the quality of the output, a principle often summarized by the phrase "garbage in, garbage out."
It highlights that effective prompting is essential for generating high-quality responses from large language models. The slide illustrates this with examples of prompting techniques, such as summarizing text or generating content in a specific style. These examples serve to clarify how different types of prompts can lead to varied outputs, reinforcing the idea that specificity in input is crucial.
The slide also introduces the concept of "prompt-based learning," which involves training a language model on a specific dataset to improve its response accuracy. This aspect is particularly relevant for organizations looking to optimize their interactions with AI tools.
An analogy is drawn between prompting an AI and asking a genie for a wish, stressing the necessity of precise phrasing to ensure the AI delivers the expected results. This analogy effectively conveys the importance of clarity and intention in prompt engineering.
Overall, the slide serves as a foundational guide for executives and teams interested in leveraging AI capabilities. It underscores the need for thoughtful prompt design to maximize the effectiveness of AI tools, providing actionable insights for enhancing business outcomes.
This PPT slide presents the RISEN framework, a structured approach designed to optimize the utilization of ChatGPT's capabilities. It outlines 5 key components: Role, Instructions, Steps, End Goal, and Narrowing. Each element serves a specific purpose in guiding the AI to produce relevant and focused outputs.
The Role section emphasizes the importance of clearly defining ChatGPT's persona, which dictates the tone and depth of the responses. This clarity ensures that the AI aligns with the user's expectations and context. Instructions follow, where detailed guidance is provided on how to approach the task at hand. This step is crucial for setting the parameters of engagement.
Steps outline a chronological breakdown of the problem-solving process, ensuring that the AI follows a logical progression. The End Goal specifies the desired outcome, which is essential for measuring the effectiveness of the interaction. Finally, the Narrowing component introduces constraints that help filter out unnecessary information, refining the focus of the output.
Case examples illustrate the practical application of the RISEN framework. For instance, using ChatGPT for competitive analysis involves directing it to assess market strengths and weaknesses, while operational efficiency tasks may require a focus on supply chain processes. Strategy formulation showcases how the framework can guide the AI in crafting comprehensive growth plans.
The prompt example further contextualizes the framework by demonstrating how to engage ChatGPT in developing an entry strategy for a specific market. Overall, the RISEN framework is positioned as a meticulous blueprint that ensures AI outputs are aligned with overarching business objectives, enhancing decision-making processes.
This PPT slide presents the "Chain of Density" (CoD) framework, a structured approach to optimizing interactions with ChatGPT. This methodology emphasizes a recursive process, which is crucial for enhancing the quality of outputs. The framework consists of 4 key components: Instructions, Recursion, Benchmark, and Additional Guidelines.
Instructions serve as the foundational step, where users define the core content or subject matter they wish to explore or elaborate on with ChatGPT. This sets the stage for the iterative refinement process. Recursion is highlighted as a fundamental principle, likening it to software development practices. It involves breaking down problems into simpler versions, allowing for a more manageable and focused approach to finding solutions. The slide illustrates this with an example of searching for a word in a dictionary, showcasing how each iteration builds upon the previous one.
The Benchmark component establishes a reference point for evaluating the initial output, ensuring that subsequent iterations can be measured against this baseline. Additional Guidelines inject further instructions or constraints, steering the iterative process toward achieving the desired outcome.
Case examples provided illustrate practical applications of the CoD framework. One example discusses refining a product description for an e-commerce platform, emphasizing the importance of incorporating feedback at each stage. Another example focuses on developing a strategic proposal, highlighting the iterative nature of revisiting and refining plans based on stakeholder input and market data.
The prompt example encourages users to apply the framework by summarizing their company's sustainability initiatives, iterating through multiple steps to enhance clarity and depth. This structured approach ultimately aims to align ChatGPT's responses more closely with business objectives, fostering a continuous improvement mindset.
This framework is developed by a team of former McKinsey and Big 4 consultants. The presentation follows the headline-body-bumper slide format used by global consulting firms.
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