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Joao Victor Souza
Joao Victor Souza

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Beyond the Basics: Advanced Prompt Engineering Techniques

#promptengineering #programming #ai

TL;DR

Prompt Engineering is not about chatting; it’s about programming. Most users get generic results because they treat LLMs like search engines. To get production-grade outputs, you must treat prompts as structured data (XML), using constraints, and verification loops.

Key Takeaways:

  • Be Specific: Vague prompts yield average results. Use Role-Based Constraints to narrow the search space.
  • Show, Don't Just Tell: Use Negative Examples to show the AI exactly what to avoid.
  • Trust, but Verify: Techniques like Chain of Verification force the model to fact-check itself, reducing hallucinations.
  • Structure Matters: Wrapping instructions in XML tags (like <Context> or <Constraints>) significantly improves adherence in modern models.

Techniques Covered:

Introduction

At its core, prompt engineering is the bridge between human intent and machine output. It is the practice of crafting inputs that constrain an AI model, steering it away from generic responses and toward a specific task. If an LLM is a powerful engine, the prompt is the steering wheel. Vague inputs result in drifting; engineered prompts drive you exactly where you need to go.

You can think of LLMs as extremely knowledgeable, capable interns who lack initiative. They have read everything but need clear instructions to act on it. If you are vague, the answer will be generic. If you apply engineering techniques, the response will be precise, aligned, and useful.

In this article, we will explore several techniques that can immediately boost your AI interactions.

Core Principles

  • Persona: Before assigning a task, you must define who the AI is supposed to be. Without a persona, the AI reverts to a "helpful, but generic, assistant."
  • Context: LLMs cannot read your mind; they can only read your text. Context provides the necessary background, constraints, and environment for the task.
  • Constraints & Format: This is where you define the boundaries. If you don't set limits, the model will guess the length, tone, and structure (often incorrectly).
  • Iteration: Prompt engineering is rarely "one and done." It is a conversation.

Techniques

Role Based Constraint Prompting

Don't just ask AI to "write code". Assign expert roles with specific constraints. This generates outputs that are significantly more robust than a generic request like "write me an ETL pipeline."

<PromptTemplate>
  <Persona>
    <Role>[specific role]</Role>
    <Experience years="[X]">[domain]</Experience>
  </Persona>

  <TaskDefinition>
    <Objective>[Specific task]</Objective>
  </TaskDefinition>

  <Requirements>
    <Constraints>
      <Constraint>[Limitation 1]</Constraint>
      <Constraint>[Limitation 2]</Constraint>
      <Constraint>[Limitation 3]</Constraint>
    </Constraints>
    <OutputFormat>[Exact format needed]</OutputFormat>
  </Requirements>
</PromptTemplate>
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Why this is effective

  • Contextual Anchoring: Specifying "X years of experience" cues the model to access a different subset of its training data. It prioritizes advanced design patterns and efficiency over beginner-level tutorials.
  • Search Space Reduction: By adding constraints, such as "Max 2GB memory," you drastically narrow the model's "search space." It stops considering generic solutions and focuses only on high-efficiency logic.
  • Ambiguity Elimination: Generic prompts produce generic code. Explicit constraints, like "Sub-100ms latency", act as guardrails, preventing the model from hallucinating an inefficient solution that technically works but fails in production.

Context Injection

When you paste a large document, LLMs often mix that information with their general training data. This technique acts as a firewall, forcing the model to ignore its outside knowledge and rely only on what you provided.

<PromptTemplate>
  <Context>
    <SourceData>
      [Paste your documentation, code, or data here]
    </SourceData>
  </Context>

  <Instructions>
    <Focus>Only use information from the <Context> section above.</Focus>
    <FailureCondition>If the answer is not in the context, state: "Insufficient information provided."</FailureCondition>
  </Instructions>

  <Task>
    [Your specific question]
  </Task>

  <Constraints>
    <Constraint>Cite specific sections/IDs when referencing facts.</Constraint>
    <Constraint>Do not use outside general knowledge.</Constraint>
  </Constraints>
</PromptTemplate>
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Why this is effective

  • The "Grounding" Effect: By explicitly forbidding "general knowledge," you switch the model's mode from "Creative Generation" to "Information Extraction." It stops trying to be smart and starts trying to be accurate.
  • The Escape Hatch: LLMs are trained to be helpful, so they hate saying "I don't know." If you don't give them a specific failure phrase (like "Insufficient information"), they will often hallucinate a plausible answer just to please you.
  • Citation Enforcing: Asking for citations forces the model to keep the source text in its active attention mechanism, reducing the chance of drift.

Constraint First Prompting

AI researchers have found that defining boundaries before defining the task prevents the model from "hallucinating" a solution that technically works but fails in practice. This stops the AI from giving you valid code that breaks your architecture.

<PromptTemplate>
  <Requirements>
    <HardConstraints>
      <Constraint>[Constraint 1]</Constraint>
      <Constraint>[Constraint 2]</Constraint>
    </HardConstraints>
    <Optimizations>
      <Preference>[Preference 1]</Preference>
    </Optimizations>
  </Requirements>

  <Execution>
    <Task>[Your actual request]</Task>
    <Instruction>Confirm you understand the constraints before generating the solution.</Instruction>
  </Execution>
</PromptTemplate>
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Why this is effective

  • Primacy Effect: By placing constraints first, you "set the state" of the model before it begins generating logic, ensuring the rules are baked into the solution from the very first token.
  • Weighting Logic: Distinguishing between "Hard" and "Soft" constraints mimics an optimization function. It tells the AI which rules are binary (Pass/Fail) and which are gradients (Better/Worse), preventing it from sacrificing a hard requirement just to make the code slightly cleaner.
  • The "Stop-and-Think" Check: The final instruction ("Confirm you understand") forces a pause. When the AI repeats the constraints back to you, it reinforces those tokens in its short-term memory, drastically increasing adherence.

Few Shot with Negative Examples

Research shows that showing a model what NOT to do is often as powerful as showing it what to do. Standard prompts give the AI a target; negative examples give it guardrails. This eliminates the "generic AI voice" that plagues most content.

<PromptTemplate>
  <TaskDefinition>
    <Description>I need you to [task].</Description>
    <Goal>Ensure quality by following the examples below.</Goal>
  </TaskDefinition>

  <FewShotExamples>
    <PositiveExamples>
      <Example>[Example 1]</Example>
      <Example>[Example 2]</Example>
    </PositiveExamples>

    <NegativeExamples>
      <Example>
        <Content>[Example 1]</Content>
        <Reasoning>Why it's bad: [Reason]</Reasoning>
      </Example>
      <Example>
        <Content>[Example 2]</Content>
        <Reasoning>Why it's bad: [Reason]</Reasoning>
      </Example>
    </NegativeExamples>
  </FewShotExamples>

  <Execution>
    <Input>[Your actual task]</Input>
  </Execution>
</PromptTemplate>
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Why this is effective

  • Boundary Setting: Positive examples define the center of the target, negative examples define the edges. This prevents the model from drifting into styles you dislike.
  • Reasoning Injection: By including the "Why it's bad" note, you aren't just giving the model data, you are teaching it your taste. It learns the logic behind your preferences, not just the pattern.
  • Pattern Breaking: LLMs are trained on the entire internet, including millions of spam emails. Without negative examples, the model might statistically default to the "average" email (which is often spammy). Negative constraints force it to prune those low-quality paths.

Structured Thinking Protocol

Research into "Chain of Thought" reasoning shows that LLMs perform significantly better when forced to show their work. This protocol forces the model to "think" in layers before responding. It stops the AI from jumping to the first (and often wrong) conclusion it finds.

<PromptTemplate>
  <MetaCognition>
    <Instruction>Before answering, follow this thinking process step-by-step:</Instruction>

    <Steps>
      <Step id="1" name="UNDERSTAND">
        <Action>Restate the core problem in your own words.</Action>
        <Action>Identify the user's underlying goal (not just the literal question).</Action>
      </Step>

      <Step id="2" name="ANALYZE">
        <Action>Break the problem into sub-components.</Action>
        <Action>Identify hidden constraints or assumptions.</Action>
      </Step>

      <Step id="3" name="STRATEGIZE">
        <Action>Outline 2-3 potential approaches.</Action>
        <Action>Briefly evaluate the trade-offs of each.</Action>
      </Step>

      <Step id="4" name="EXECUTE">
        <Action>Provide the final solution based on the best strategy.</Action>
      </Step>
    </Steps>
  </MetaCognition>

  <Execution>
    <Input>[Your question]</Input>
  </Execution>
</PromptTemplate>
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Why this is effective

  • Latency for Logic: By forcing the model to write out the "Understand" and "Analyze" sections, you are literally buying it more compute time (tokens) to process the logic before it commits to an answer.
  • Context Extraction: The prompt forces the model to explicitly acknowledge variables like "5-person team" in the Analyze phase. Once written down, the model is much less likely to ignore them in the final answer.
  • Self-Correction: Often, when an LLM outlines "Trade-offs" in the strategy phase, it realizes the complex solution is bad. This step acts as a filter, preventing the model from recommending "best practices" that don't fit your specific context.

Multi Perspective Prompting

Standard prompts often yield "yes-man" answers. To get critical analysis, you must force the AI to simulate a debate. This technique mimics a boardroom meeting, requiring the model to wear multiple "hats" before synthesizing a conclusion.

<PromptTemplate>
  <TaskDefinition>
    <Action>Analyze [topic/problem] from distinct perspectives.</Action>
  </TaskDefinition>

  <AnalysisFramework>
    <Perspective type="Technical Feasibility">
      <Focus>[specific constraints, stack, complexity]</Focus>
    </Perspective>

    <Perspective type="Business Impact">
      <Focus>[ROI, velocity, market positioning]</Focus>
    </Perspective>

    <Perspective type="User Experience">
      <Focus>[friction, latency, accessibility]</Focus>
    </Perspective>

    <Perspective type="Risk & Security">
      <Focus>[compliance, data loss, vendor lock-in]</Focus>
    </Perspective>
  </AnalysisFramework>

  <SynthesisInstructions>
    <Step>Review the perspectives above.</Step>
    <Step>Identify conflicting goals and key trade-offs.</Step>
    <FinalOutput>
      <Requirement>Provide a final recommendation based on strongest evidence.</Requirement>
      <Requirement>Clearly state what is gained and what is sacrificed.</Requirement>
    </FinalOutput>
  </SynthesisInstructions>
</PromptTemplate>
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Why this is effective

  • Breaking the "Yes-Man" Cycle: LLMs prioritize probability and agreeableness. By explicitly asking for conflicting perspectives, you force the model to critique its own potential solution.
  • Orthogonal Thinking: It ensures the model considers opposing goals (e.g., Speed vs. Security) simultaneously, rather than sequentially.
  • Forced Realism: A common failure mode for LLMs is trying to say "we can have it all." The synthesis instruction forces it to acknowledge the downside of its recommendation.

Chain of verification

Developed by researchers at Meta AI, this technique is the "spell checker" for facts. It combats hallucinations by forcing the model to cross-examine itself before showing you the result. Research indicates this can significantly reduce hallucinations on complex factual queries.

<PromptTemplate>
  <ProcessMethodology type="Fact-Checking">
    <Phase sequence="1" name="Draft">
      <Instruction>Provide your initial answer to the task.</Instruction>
    </Phase>

    <Phase sequence="2" name="Verify">
      <Instruction>Generate 3-5 factual verification questions that check the claims in your draft.</Instruction>
    </Phase>

    <Phase sequence="3" name="Answer">
      <Instruction>Answer those verification questions independently.</Instruction>
    </Phase>

    <Phase sequence="4" name="Refine">
      <Instruction>Rewrite the initial answer using only the verified facts.</Instruction>
    </Phase>
  </ProcessMethodology>

  <Execution>
    <Task>[Your question]</Task>
  </Execution>
</PromptTemplate>
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Why this is effective

  • Adversarial relationship: It forces the model to act as both the "Writer" and the "Editor." The "Writer" mode is creative and prone to hallucinations; the "Editor" mode is analytical and critical.
  • Fact Isolation: By breaking the draft down into specific verification questions (e.g., "What year was X released?"), the model can retrieve specific facts more accurately than it can when generating a long narrative.
  • The "Correction Loop": Most hallucinations happen because the model commits to a wrong fact early in the sentence and then "doubles down" to keep the sentence coherent. This technique gives the model a chance to back out of those errors before the user sees them.

Meta Prompting

Instead of guessing the magic words, this technique forces the model to become its own Prompt Engineer, designing the optimal instructions for itself before executing them. This is like hiring a senior engineer to translate your vague idea into a technical spec.

<PromptTemplate>
  <UserObjective>
    <Goal>[High-level objective]</Goal>
  </UserObjective>

  <MetaTask>
    <Phase sequence="1" name="Analyze">
      <Action>Break down the goal into necessary steps.</Action>
      <Action>Identify required context and constraints.</Action>
    </Phase>

    <Phase sequence="2" name="Design">
      <Action>Write the perfect prompt to achieve this goal.</Action>
      <Requirements>
        <Requirement>Be explicit.</Requirement>
        <Requirement>Use expert personas.</Requirement>
        <Requirement>Define specific output formats.</Requirement>
      </Requirements>
    </Phase>

    <Phase sequence="3" name="Execute">
      <Action>Immediately run the designed prompt.</Action>
      <Action>Provide the final result.</Action>
    </Phase>
  </MetaTask>
</PromptTemplate>
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Why this is effective

  • Bridging the Gap: Humans are often bad at explaining technical requirements. AI is excellent at it. This technique lets the AI translate your "human intent" into "machine instructions."
  • Vocabulary Match: The model selects words and structures that it "knows" will trigger the best response from its own neural network—words you might not have thought to use.
  • Completeness: When you ask the AI to "analyze what is needed," it will often remember edge cases (like "Error Handling" or "API Rate Limits") that you forgot to ask for in your original request.

Conclusion

The evolution from a casual AI user to a Prompt Engineer happens when you stop accepting the first answer. The techniques outlined here—from Context Injection to Chain of Verification—are designed to solve the biggest problem with LLMs today: unreliability.

By adding constraints and structure, you transform the AI from a creative (but messy) brainstormer into a precise tool that fits into your production workflows.

Here is a quick reference guide to help you stabilize your outputs:

If you want to... Use this technique...
Stop generic answers Role-Based Constraints
Fix hallucinations Chain of Verification & Context Injection
Get complex logic/reasoning Structured Thinking Protocol
Ensure strict code/format Constraint-First Prompting
Get critical/strategic advice Multi-Perspective Prompting
Avoid specific bad habits Negative Examples
Build a tool/script Meta-Prompting

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