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Prompts are not simple questions.

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They are programs that control AI behavior.

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Just as a single line of code can dramatically change how an application behaves, a small change in

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prompt wording can completely alter what an AI produces.

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Understanding this idea is the foundation of effective, prompt engineering.

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Well-designed prompts improve three critical dimensions of AI output accuracy, consistency, and safety.

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Accuracy improves because the model better understands what you want.

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Consistency improves because structured prompts reduce randomness across interactions.

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Safety improves because clear constraints reduce harmful or unexpected responses.

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Think of prompt engineering as interface design for AI behavior.

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You are designing the control panel that determines how the model interprets every request.

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Without thoughtful, prompt design, even the most powerful models produce unreliable results.

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This slide sets the mindset for the entire section.

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Prompt engineering is not trial and error or guesswork.

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It is a disciplined engineering practice.

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When prompts are treated as first class system components, AI behavior becomes predictable, controllable,

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and production ready instead of fragile and surprising.

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A well-designed prompt is built from three distinct components a system role, the user role, and the

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assistant role.

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Each serves a different purpose, and separating them gives you precise control over AI behavior.

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The system role establishes the model's behavior, personality, and operational constraints.

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It defines how the AI should act before any user interaction begins.

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The user role contains the actual task or request what you want the AI to do right now.

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The assistant role captures the model's previous responses, maintaining conversational memory across

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multiple turns.

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This structure creates a clear separation of concerns.

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The system rule sets the rules, the user role provides dynamic input, and the assistant role preserves

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context.

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Without this separation, prompts become cluttered, unreliable, and difficult to maintain.

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Understanding this anatomy is essential for building scalable AI systems.

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It allows you to enforce rules, consistently guide behavior without repetition, and maintain context

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cleanly.

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For engineers, this structure transforms prompting from ad hoc experimentation into deliberate system

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design.

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The system rule is the most powerful tool you have for shaping AI behavior.

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It defines the model's personality, expertise, level, tone, and operational boundaries before any

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user interaction takes place.

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Think of it as the AI's constitution.

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A stable set of rules that governs every response.

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Unlike user prompts, which change with each request, system prompts remain constant throughout a session.

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This stability is what enables consistent behavior across interactions.

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For example, you might define the AI as a senior software engineer who provides technically accurate

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answers with code examples, or enforce strict safety guidelines that prohibit harmful instructions.

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Best practice is to keep system prompts stable, and intentional changes to the system rule should be

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rare and deliberate because they affect the entire conversation.

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Overusing or constantly modifying system prompts leads to unpredictable behavior.

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When used correctly, the system role reduces the need to repeat rules in every prompt.

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It's centralizes behavior control, improves alignment, and dramatically increases reliability for

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production systems.

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A well-designed system role is Non-negotiable.

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The user and assistant roles form the conversational layer of a prompt.

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The user role contains the specific task, question, or instruction you want the AI to handle.

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This is where clarity and precision matter most.

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Well-designed user prompts are specific, structured, and provide necessary context upfront.

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Complex requests should be broken into steps rather than packed into a single sentence.

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At the same time, user prompts should avoid embedding behavioral rules.

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Those belong in the system role.

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Mixing responsibilities makes prompts harder to reason about and maintain.

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The assistant role captures the model's previous responses.

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This history allows the AI to maintain context across multiple turns.

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Reference earlier outputs and maintain consistent formatting or reasoning without assistant history.

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Every interaction becomes stateless and disconnected.

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Together, these roles enable multi-turn problem solving, iterative refinement, and coherent dialogue.

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Engineers who understand and respect these boundaries build systems that feel intelligent and consistent,

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rather than erratic or forgetful.

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Zero shot prompting is the simplest and most token efficient prompting strategy.

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In this approach, you provide no examples and rely entirely on the model's pretraining to complete

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the task.

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This makes zero shot, prompting fast, cheap, and easy to implement.

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Zero shot prompting works best for straightforward tasks with clear instructions.

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Common examples include answering factual questions, summarizing simple text, or performing well-known

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transformations.

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Because it uses minimal tokens, it is ideal for cost sensitive and real time applications.

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However, zero shot prompting has limitations.

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It is less reliable for complex tasks, strict formatting requirements, or domain specific outputs

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without examples.

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The model may interpret instructions differently than you expect.

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The engineering mindset is to start with zero shot prompting as a baseline test, whether it meets your

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requirements before adding complexity.

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Many teams overuse few shot prompting when zero shot would have been sufficient.

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Efficient prompt design always begins with the simplest, viable approach one shot and few shot, prompting.

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Improve reliability by providing examples that demonstrate the desired input output pattern.

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In one shot prompting, you include a single example.

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In few shot prompting, you include multiple examples, typically between 2 and 5 examples.

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Give the model concrete guidance on formatting, structure, and task interpretation.

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This dramatically improves consistency, especially for complex tasks or structured outputs.

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Few shot prompting is particularly effective when outputs must follow a strict schema, or when edge

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cases need to be handled correctly.

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The trade off is cost and latency.

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Each example consumes tokens.

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Increasing both inference cost and response time for high traffic systems.

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This trade off must be carefully evaluated.

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The T is intentional use.

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Few shot prompting should not be the default.

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It is a tool to be applied when zero shot prompting fails to meet accuracy or consistency requirements.

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Engineers must balance quality against efficiency.

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Choosing the minimal number of examples needed to achieve reliable results.

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Choosing the right prompting strategy is not guesswork.

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It is an engineering decision based on measurable constraints.

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Three factors should guide your choice.

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Task complexity.

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Structure requirements and cost or latency constraints.

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Simple tasks with clear goals often work well with zero shot prompting.

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As complexity increases or when outputs must follow a strict format, one shot or few shot prompting

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becomes valuable.

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Examples reduce ambiguity and guide the model toward consistent behavior.

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Cost and latency also matter.

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Few shot prompting improves quality but increases token usage.

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In mission critical systems, this trade off is often worth it in real time or cost sensitive systems,

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efficiency may take priority.

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A proven workflow is to start with zero shot prompting, evaluate output quality and introduce examples

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only if necessary.

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Prompt design is iterative engineering, not trial and error.

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Measure results, refine prompts, and optimize examples based on evidence.

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The key takeaway is simple effective prop design is systematic, intentional, and grounded in real

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constraints, just like any other engineering discipline.