Stop paying for designers—start Prompting! 🚀

Design a '4:5' product poster for an 'orange' juice 3d bottle using playful inflatable-plastic packaging surrealism where the bottle behaves like a squeezed toy object mid-pressure. The poster should communicate juiciness, tension, and tactile freshness through a bottle that visibly bulges, stretches, and compresses like a soft object being squeezed from the inside out.
SUBJECT:
A single bottle dominates the center-left, illustrated in a semi-3D stylized form (not photoreal). The bottle is visibly distorted—its midsection bulges outward while the neck is slightly compressed, as if internal juice pressure is pushing against the container walls. The liquid inside exaggerates this effect, forming rounded convex surfaces pressing against the plastic. The cap is slightly tilted frompressure. The bottle feels elastic, alive, and reactive rather than rigid.
COMPOSITION:
The composition mechanic is “internal pressure distortion”. The eye enters through the most inflated part of the bottle (center bulge), then follows curved tension lines outward toward stretched typography on the right side. The camera uses a slightly low, close-up perspective with mild fisheye distortion, amplifying the sense of pressure and expansion. The bottle leans diagonally into the frame as if pushing against invisible resistance. Negative space on the right is intentionally stretched and warped, echoing the bottle’s deformation.
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Everyone ...🌹

May this afternoon blessed
and happy ....🥰

Lesson 13.
121. Longer sentences can sometimes produce suboptimal results.
122. It’s best to break long sentences in a prompt into a series of shorter sentences and simpler tasks.
123. So, let’s return to Sasha, and use what we have learned so far.
124. Sasha updates her prompt to: “You're a cloud architect. You want to build a Google Cloud VPC network that can be centrally managed. You also connect to other
125. VPC networks in your company's other regions. You don't want to have many different sets of firewall policies to maintain. What sort of network architecture would you recommend?”
126. With this new prompt, Gemini proposes a hub-and-spoke network architecture, which fits Sasha’s needs exactly.
127. By refining and amending her prompts, Sasha has articulated her requirements in a way that Gemini can respond with the correct focus and level of detail.

Lesson 12
111. The more vague the prompt, the more chance that the model will produce a result that is not usable.
112. Be clear and concise in the prompts that you feed into the model.
113. Next, be sure to define boundaries for the prompt.
114. It’s better to instruct the model on what to do rather than what not to do.
115. If the model gets stuck, give it a few 'fallback' outputs that work in various situations.
116. For example, something like "I'm still learning about that" to use when unsure.
117. Another best practice is to adopt a persona for your input.
118. Adding a persona for the model can provide meaningful context to help it focus on related questions, which can help improve accuracy.
119. This prompt would help Sasha, the cloud architect, get started with prototyping a network architecture for Cymbal Bank.
120. And finally, it’s a recommended practice to keep each sentence concise.
To be continued…lesson 13

Lesson 11
101. The element order can also change
102. Let's amend Sasha’s original prompt “How can I create a network that uses IPv4 and IPv6 addresses?” and add a role context to the input fed into Gemini.
103. She also adds the detail of needing a dual stack subnet.
104. The new prompt is “I want you to act as a cloud architect in Google
105. Cloud. How can I use gcloud to create a network that uses IPv4 and IPv6 subnets?”
106. But since Gemini maintains its own interaction context, she could have just asked “I want you to act as a cloud architect
107. in Google Cloud. How can I adjust the gcloud command provided to create a subnet to ensure the subnet is dual stack?”
108. Now that you’ve had a chance to explore what Gen AI is, what large language models are
109. and how they’re trained, and what prompt engineering is, it’s time to explore some prompt engineering best practices.
110. The first best practice is to write detailed and explicit instructions.
To be continued…lesson 12

Lesson 10.
91. In our example, we state “I want you to act as a business professor. I’ll give you a term, and you will correctly explain its
92. meaning. Make sure your answers are always right. What is ROI? “ For Sasha’s needs, using role prompts might be the best solution. She can define
93. what is required and in what context. This means that the LLM will have a clear point of reference when supplying an answer. Now that you’ve
94. seen the types of prompts you can create, let’s explore the two elements of a prompt: the preamble and the input. The preamble refers to the
95. introductory text you provide to give the model context and instructions before your main question or request. Think of it as setting the stage for the
96. LLM to better understand what you want. It can include the context for the task, the task itself, and some examples to guide the model. The
97. input is the central request you're making to the LLM. It’s what the instruction or task will act upon, for example “Comment: I don’t know what
98. to think about the video. The review is:” Based on the preamble, Gemini reviews the input and suggests if the review is positive, neutral, or negative.
100. It is worth noting that not all the components are required for a prompt, and the format can change depending on the task at hand.
To be continued…lesson 11

Lesson 9

81. Prompts can be in the form of a question, and are categorized into four categories: zero-shot, one-shot, few-shot, and role prompts.
82. Zero-shot prompts do not contain any context or examples to assist the model
83. For example, the prompt “What’s the capital of France?” does not provide any examples of what a capital is.
84. Clearly, that is not too important for this example.
85. But for more specific and technical prompts, an example would help refine the scope of the response from Gemini.
86. One-shot prompts, however, provide one example to the model for context.
87. Here, we ask for the capital of France again, but we provide Italy and Rome as an example.
88. And few-shot prompts provide at least two examples to the model for context.
89. Here, our prompt is updated to also include Japan and Tokyo in our examples.
90. And then, there are role prompts which require a frame of reference for the model to work from as it answers the questions.
To be continued…lessonn10

Lesson 8
71. She just needs to articulate her needs in a way that gets the best response from Gemini.
72. For example, if she uses the prompt “How can I create a network that uses
73. For example, if she uses the prompt “How can I create a network that uses
74. IPv4 and IPv6 addresses?”, she will get a response that details how to do just that
75. You’ve learned that a large language model is a huge object model containing a massive dataset of text.
76. But how can you extract the information you need from this dataset?
77. This is where prompt engineering comes in.
78. A prompt is the text that you feed to the model, and prompt engineering is a way of articulating your prompts to get the best response from the model.
79. The better structured a prompt is, the better the output from the model will be
80. Let’s explore what this means
To be continued..lesson 9