# Prompt Engineering for Beginners: Practical Concepts, Techniques, and Exercises

## Table of Contents

### 1. Introduction

- [1.1 Executive Summary](#11-executive-summary)
- [1.2 Learning Objectives](#12-learning-objectives)
- [1.3 Prerequisite Knowledge](#13-prerequisite-knowledge)
- [1.4 How to Use This Guide](#14-how-to-use-this-guide)


### 2. Core Concepts

- [2.1 What is Prompt Engineering?](#21-what-is-prompt-engineering)
- [2.2 The Anatomy of a Prompt](#22-the-anatomy-of-a-prompt)
- [2.3 How Large Language Models Process Prompts](#23-how-large-language-models-process-prompts)
- [2.4 Types of Prompting](#24-types-of-prompting)


### 3. Fundamental Techniques

- [3.1 Zero-Shot Prompting](#31-zero-shot-prompting)
- [3.2 Few-Shot Prompting](#32-few-shot-prompting)
- [3.3 Chain-of-Thought Prompting](#33-chain-of-thought-prompting)
- [3.4 Role-Based Prompting](#34-role-based-prompting)


### 4. Advanced Techniques

- [4.1 Instruction Engineering](#41-instruction-engineering)
- [4.2 Context Setting and Constraints](#42-context-setting-and-constraints)
- [4.3 Output Formatting](#43-output-formatting)
- [4.4 Iterative Refinement](#44-iterative-refinement)


### 5. Practical Step-by-Step Examples

- [5.1 Content Creation Workflows](#51-content-creation-workflows)
- [5.2 Data Analysis and Extraction](#52-data-analysis-and-extraction)
- [5.3 Code Generation and Debugging](#53-code-generation-and-debugging)
- [5.4 Problem Solving and Reasoning](#54-problem-solving-and-reasoning)


### 6. Common Pitfalls and Best Practices

- [6.1 Typical Mistakes to Avoid](#61-typical-mistakes-to-avoid)
- [6.2 Best Practices for Effective Prompting](#62-best-practices-for-effective-prompting)
- [6.3 Debugging and Troubleshooting](#63-debugging-and-troubleshooting)


### 7. Ethical Considerations

- [7.1 Responsible AI Usage](#71-responsible-ai-usage)
- [7.2 Bias Mitigation](#72-bias-mitigation)
- [7.3 Privacy and Security](#73-privacy-and-security)


### 8. Tools and Workflows

- [8.1 Prompt Management Systems](#81-prompt-management-systems)
- [8.2 Testing and Evaluation](#82-testing-and-evaluation)
- [8.3 A/B Testing Methodology](#83-ab-testing-methodology)


### 9. Practice Exercises

- [9.1 Beginner Exercises](#91-beginner-exercises)
- [9.2 Intermediate Challenges](#92-intermediate-challenges)
- [9.3 Advanced Projects](#93-advanced-projects)


### 10. Reference Materials

- [10.1 Glossary](#101-glossary)
- [10.2 Templates and Checklists](#102-templates-and-checklists)
- [10.3 Further Reading](#103-further-reading)
- [10.4 Bibliography](#104-bibliography)

***

## 1. Introduction

### 1.1 Executive Summary

**Prompt engineering** has emerged as one of the most critical skills in the artificial intelligence era. As large language models (LLMs) like GPT-4, Claude, and Gemini become increasingly powerful and accessible, the ability to communicate effectively with these systems determines the quality and usefulness of their outputs. This comprehensive guide introduces beginners to the fundamental concepts, practical techniques, and best practices of prompt engineering, providing a structured pathway from basic understanding to advanced implementation.

The importance of prompt engineering cannot be overstated. Research indicates that 78% of AI project failures stem from poor prompt design rather than model limitations. With the global prompt engineering market expected to reach \$505 billion by 2034, growing at 33% CAGR, mastering these skills has become essential for professionals across industries. Whether you're a developer seeking to integrate AI into applications, a content creator looking to enhance productivity, or a business professional aiming to leverage AI for decision-making, effective prompting is your gateway to unlocking the full potential of modern AI systems.[^1]

This guide takes a practical, hands-on approach to learning prompt engineering. Rather than focusing solely on theoretical concepts, it provides **15+ ready-to-use prompts**, step-by-step refinement processes, and real-world examples that you can immediately apply. The content is structured progressively, starting with fundamental concepts and building toward advanced techniques, ensuring that readers develop both understanding and practical skills.

**Key benefits of mastering prompt engineering include:**

- **Enhanced accuracy**: Well-crafted prompts can improve AI output quality by up to 40%[^2]
- **Increased efficiency**: Effective prompting reduces the need for multiple iterations, saving time and computational resources
- **Better control**: Precise instructions enable more predictable and aligned AI responses
- **Cost optimization**: Optimized prompts can reduce API costs by minimizing token usage and retry attempts


### 1.2 Learning Objectives

By completing this guide, you will be able to:

**Foundational Knowledge:**

- Define prompt engineering and explain its role in AI applications
- Identify the key components of effective prompts
- Understand how LLMs process and respond to different prompt structures

**Technical Skills:**

- Write clear, specific, and effective prompts for various tasks
- Apply zero-shot, few-shot, and chain-of-thought prompting techniques
- Design role-based prompts that leverage persona-driven responses
- Structure complex instructions using proven frameworks

**Practical Applications:**

- Create prompts for content generation, data analysis, and code development
- Implement prompt testing and evaluation methodologies
- Debug and refine prompts through iterative improvement processes
- Integrate prompt engineering best practices into professional workflows

**Advanced Techniques:**

- Develop systematic approaches to prompt optimization
- Apply A/B testing methodologies to compare prompt effectiveness
- Implement ethical considerations and bias mitigation strategies
- Design scalable prompt management systems for team collaboration


### 1.3 Prerequisite Knowledge

This guide is designed for beginners, requiring minimal technical background. However, familiarity with the following concepts will enhance your learning experience:

**Essential Prerequisites:**

- Basic understanding of what artificial intelligence and machine learning are
- Familiarity with text-based interfaces (you've used chatbots or text editors)
- Comfort with reading and writing in English

**Recommended but not required:**

- Experience using AI tools like ChatGPT, Claude, or similar platforms
- Basic understanding of how software applications work
- Familiarity with common file formats (JSON, CSV, markdown)

**For advanced sections:**

- Basic programming concepts (variables, functions, logic)
- Understanding of web APIs and HTTP requests (for tool integration)
- Familiarity with data analysis concepts


### 1.4 How to Use This Guide

This guide is structured for multiple learning paths:

**Sequential Learning Path (Recommended for beginners):**
Start with Section 1 and progress through each section in order. This approach ensures you build foundational knowledge before tackling advanced concepts.

**Topic-Focused Learning:**
If you have specific needs, you can jump to relevant sections:

- For immediate practical application: Start with Section 5 (Practical Examples)
- For troubleshooting existing prompts: Begin with Section 6 (Common Pitfalls)
- For team implementation: Focus on Section 8 (Tools and Workflows)

**Practice-Oriented Learning:**
Complete exercises in Section 9 alongside reading each chapter to reinforce learning through hands-on experience.

**Interactive Elements:**

- **📝 Try This**: Practical exercises to test concepts immediately
- **⚠️ Common Mistake**: Warnings about frequent errors
- **💡 Pro Tip**: Advanced insights from expert practitioners
- **🔧 Template**: Ready-to-use prompt templates

***

## 2. Core Concepts

### 2.1 What is Prompt Engineering?

**Prompt engineering** is the practice of designing, refining, and optimizing instructions given to artificial intelligence models to elicit desired responses. Unlike traditional programming where we write code in specific syntax, prompt engineering uses natural language to communicate with AI systems, making it accessible to non-programmers while requiring its own set of skills and best practices.[^3]

At its essence, prompt engineering bridges the gap between human intent and machine understanding. When you interact with an AI model, your prompt serves as both the question and the instruction manual, guiding the AI toward producing useful, accurate, and relevant outputs. The quality of your prompt directly influences the quality of the response - a well-crafted prompt can mean the difference between a vague, generic output and one that precisely meets your needs.[^2]

**The Evolution of Prompt Engineering**

Prompt engineering emerged as large language models demonstrated remarkable capabilities in understanding and generating human-like text. Early AI systems required extensive programming and training for specific tasks. Modern LLMs, however, can adapt to new tasks through carefully crafted prompts, a capability known as "in-context learning". This breakthrough has democratized AI access, allowing anyone with strong communication skills to leverage powerful AI systems.[^4]

The field has rapidly evolved from simple question-and-answer interactions to sophisticated frameworks involving multi-step reasoning, role-based personas, and complex instruction hierarchies. Leading AI companies like OpenAI, Anthropic, and Google have invested heavily in research to understand how different prompting strategies affect model performance.[^5][^6]

**Why Prompt Engineering Matters**

Research from major AI labs consistently shows that prompt quality significantly impacts model performance:

- **Accuracy**: Anthropic's research demonstrates that well-engineered prompts can improve task accuracy by 22% compared to basic prompts[^5]
- **Consistency**: Structured prompts produce more reliable outputs across multiple runs[^7]
- **Cost Efficiency**: Optimized prompts reduce token usage and minimize the need for multiple attempts[^5]
- **Safety**: Thoughtfully designed prompts help mitigate harmful or biased outputs[^8]


### 2.2 The Anatomy of a Prompt

Understanding the structure of effective prompts is crucial for creating clear, actionable instructions. Based on research from AWS and Anthropic, effective prompts typically contain several key components:[^7]

**1. Task Context (Role/Persona Assignment)**
This section establishes who the AI should act as and the broad context for the task.

```
Example: "You are a helpful customer support agent specializing in troubleshooting technical issues with computers."
```

**2. Tone Context**
Sets the communication style and approach the AI should adopt.

```
Example: "Use a friendly, professional tone. Be patient and explain technical concepts in simple terms."
```

**3. Background Data (Context)**
Provides necessary information for the AI to complete the task effectively.

```
Example: "The customer is experiencing slow internet speeds. Their connection speed test shows 15 Mbps download and 2 Mbps upload. They have a cable internet plan rated for 100 Mbps."
```

**4. Detailed Task Description and Rules**
Specific instructions about what the AI should accomplish and how.

```
Example: "Provide a troubleshooting checklist with exactly 5 steps. Each step should include the action and why it helps. Number each step clearly."
```

**5. Examples (When Applicable)**
Demonstrations of the desired output format or approach.

```
Example: "Format like this:
1. [Action] - [Reason why this helps]
2. [Action] - [Reason why this helps]"
```

**6. Output Formatting**
Specifications for how the response should be structured.

```
Example: "End your response with a brief summary of what the customer should expect after completing these steps."
```

**7. Constraints and Limitations**
Clear boundaries on what the AI should or shouldn't do.

```
Example: "Do not recommend opening the computer case or handling internal components. If hardware replacement seems necessary, advise contacting a technician."
```


### 2.3 How Large Language Models Process Prompts

To write effective prompts, it's helpful to understand how LLMs interpret and respond to instructions. Modern language models use a process called **autoregressive prediction** - they predict the next word (or token) based on all previous context, then use that prediction to inform the next word, continuing until the response is complete.[^9]

**The Processing Pipeline:**

1. **Tokenization**: The prompt is broken into smaller units called tokens (roughly equivalent to words or word parts)
2. **Context Assembly**: The model considers the entire prompt as context for generating the response
3. **Pattern Recognition**: Based on training data, the model identifies patterns similar to the prompt structure
4. **Response Generation**: The model generates text token by token, maintaining consistency with the established context
5. **Coherence Checking**: Advanced models perform internal consistency checks to maintain logical flow

**Key Implications for Prompt Design:**

- **Order Matters**: Information presented earlier in the prompt has stronger influence on the response
- **Specificity Helps**: Detailed instructions reduce ambiguity and improve response quality
- **Context Limitations**: Models have context windows (typically 4,000-128,000 tokens) that limit how much information they can consider
- **Pattern Matching**: Models respond well to familiar structures and formats from their training data


### 2.4 Types of Prompting

Different tasks require different prompting approaches. Understanding these categories helps you choose the most effective strategy for your specific needs.

**Zero-Shot Prompting**
The model performs a task without any examples, relying solely on its training knowledge.[^10]

```
Prompt: "Classify the sentiment of this review: 'The product exceeded my expectations and arrived quickly.'"
Response: "Positive"
```

**Few-Shot Prompting**
The model learns from a few examples provided in the prompt.[^11]

```
Prompt: "Classify sentiment:
Review: 'Great service, very satisfied!' → Positive
Review: 'Poor quality, disappointed.' → Negative
Review: 'The product is okay, nothing special.' → ?"
```

**Chain-of-Thought Prompting**
The model shows its reasoning process step-by-step.[^12]

```
Prompt: "Solve this step by step: A store has 15 apples. They sell 8 in the morning and 4 in the afternoon. How many are left?"
```

**Role-Based Prompting**
The model adopts a specific persona or expertise area.[^7]

```
Prompt: "You are a financial advisor. Explain the difference between stocks and bonds to a complete beginner."
```

Each approach has its strengths:

- **Zero-shot** is fastest and simplest for straightforward tasks
- **Few-shot** provides better control and consistency for specific formats
- **Chain-of-thought** improves accuracy on complex reasoning tasks
- **Role-based** leverages domain expertise for specialized topics

***

## 3. Fundamental Techniques

This section introduces the four essential prompt engineering techniques that form the foundation of effective AI interaction. Mastering these approaches will enable you to handle the majority of common prompting scenarios with confidence.

### 3.1 Zero-Shot Prompting

Zero-shot prompting is the most straightforward approach to interacting with LLMs. In this technique, you provide a clear instruction without examples, relying on the model's pre-trained knowledge to understand and execute the task.[^13][^10]

**When to Use Zero-Shot Prompting:**

- Simple, well-defined tasks
- When you need quick results
- For tasks the model has likely seen during training
- When providing examples would be time-consuming or impractical

**Key Principles for Effective Zero-Shot Prompts:**

**1. Be Specific and Direct**
Vague prompts lead to unpredictable results. Instead of asking "Tell me about dogs," ask "List five key characteristics that make Golden Retrievers good family pets."

**2. Use Clear Action Verbs**
Start with precise verbs that indicate exactly what you want: "Summarize," "Explain," "List," "Compare," "Generate," "Analyze."

**3. Provide Sufficient Context**
Include relevant background information that helps the model understand the scope and requirements of the task.

**📝 Try This - Zero-Shot Examples:**

**Example 1: Text Classification**

```
Prompt: "Classify the following email as 'Urgent,' 'Important,' or 'Routine':
'Hi team, the client presentation has been moved to tomorrow at 9 AM. Please review the slides and send feedback by tonight.'"

Expected Output: "Important"
```

**Example 2: Content Generation**

```
Prompt: "Write a professional email subject line for a quarterly business review meeting scheduled for next Friday."

Expected Output: "Quarterly Business Review - Friday [Date] Meeting"
```

**Example 3: Problem Solving**

```
Prompt: "A restaurant wants to reduce food waste. Suggest three practical strategies they could implement immediately."

Expected Output: A list of three actionable strategies with brief explanations.
```

**💡 Pro Tip:** Add the phrase "Let's think step by step" at the end of complex zero-shot prompts to encourage more thorough reasoning.[^14]

**Common Zero-Shot Patterns:**

**Classification Pattern:**

```
"Classify [item] as [category options]: [item description]"
```

**Generation Pattern:**

```
"Create/Generate/Write [specific output] for [context/purpose]"
```

**Analysis Pattern:**

```
"Analyze [subject] and identify [specific aspects to focus on]"
```


### 3.2 Few-Shot Prompting

Few-shot prompting involves providing the model with 2-5 examples that demonstrate the desired input-output pattern. This technique is particularly powerful for tasks requiring specific formatting, style, or approach that might not be immediately clear from instructions alone.[^15][^16]

**When to Use Few-Shot Prompting:**

- When you need consistent output formatting
- For tasks with specific style requirements
- When zero-shot results are inconsistent
- For domain-specific applications with unique patterns

**Structure of Few-Shot Prompts:**

1. **Task instruction** (optional but recommended)
2. **Examples** (2-5 demonstrations)
3. **New input** to be processed

**📝 Try This - Few-Shot Examples:**

**Example 1: Product Description Generation**

```
Prompt: "Generate product descriptions in this format:

Product: Wireless Bluetooth Earbuds
Description: Experience premium sound quality with our sleek wireless earbuds. Features 8-hour battery life, noise cancellation, and water resistance. Perfect for workouts and daily commutes.

Product: Smart Fitness Watch  
Description: Track your health goals with precision using our advanced fitness watch. Monitors heart rate, sleep patterns, and 50+ workout modes. Stylish design meets cutting-edge technology.

Product: Portable Phone Charger
Description:"
```

**Example 2: Data Extraction**

```
Prompt: "Extract key information from customer feedback:

Feedback: 'Great product, fast shipping, but the packaging was damaged.'
Extracted: Product Quality: Positive | Shipping: Positive | Packaging: Negative

Feedback: 'Love the design and features, but it's quite expensive for what you get.'  
Extracted: Design: Positive | Features: Positive | Price: Negative

Feedback: 'Decent quality, arrived on time, fair price overall.'
Extracted:"
```

**Best Practices for Few-Shot Prompting:**

**1. Choose Diverse Examples**
Select examples that cover different scenarios or edge cases your real inputs might encounter.

**2. Maintain Consistent Formatting**
Ensure all examples follow exactly the same structure and format you want in the output.

**3. Order Examples Strategically**
Place your best or most representative example last, as models tend to be influenced more by recent context.

**4. Include Edge Cases**
If applicable, include examples that show how to handle unusual or boundary conditions.

**⚠️ Common Mistake:** Using too many examples (more than 5-7) can confuse the model and exceed context limits. Start with 2-3 high-quality examples.

### 3.3 Chain-of-Thought Prompting

Chain-of-thought (CoT) prompting encourages the model to show its reasoning process step-by-step before arriving at a final answer. This approach significantly improves performance on complex reasoning tasks and provides transparency into the model's decision-making process.[^17][^12]

**When to Use Chain-of-Thought Prompting:**

- Mathematical problems and calculations
- Multi-step reasoning tasks
- Complex analysis requiring logic
- When you need to verify the reasoning process
- For debugging incorrect results

**Types of Chain-of-Thought Prompting:**

**1. Zero-Shot Chain-of-Thought**
Simply add "Let's think step by step" to your prompt.

```
Prompt: "A company's revenue increased from $2 million to $2.8 million. What was the percentage increase? Let's think step by step."

Expected Response:
"Let me calculate this step by step:
1. Find the increase amount: $2.8M - $2.0M = $0.8M
2. Calculate percentage: ($0.8M ÷ $2.0M) × 100
3. Solve: 0.4 × 100 = 40%
The percentage increase was 40%."
```

**2. Few-Shot Chain-of-Thought**
Provide examples that show the reasoning process.

```
Prompt: "Solve these problems step by step:

Problem: If 3 apples cost $2, how much do 7 apples cost?
Solution: 
- Cost per apple: $2 ÷ 3 = $0.67
- Cost of 7 apples: $0.67 × 7 = $4.67

Problem: A recipe for 4 people needs 2 cups of flour. How much flour for 10 people?
Solution:
- Flour per person: 2 cups ÷ 4 people = 0.5 cups per person  
- Flour for 10 people: 0.5 × 10 = 5 cups

Problem: A parking meter costs $1.50 per hour. How much for 2.5 hours?
Solution:"
```

**📝 Try This - Chain-of-Thought Applications:**

**Example 1: Business Decision Analysis**

```
Prompt: "Should our company launch a new product line? Here's the data:
- Development cost: $500,000
- Expected monthly revenue: $50,000  
- Monthly operating costs: $20,000
- Market research shows 60% success rate for similar products

Analyze this step by step and provide a recommendation."
```

**Example 2: Technical Troubleshooting**

```
Prompt: "A website is loading slowly. Given these symptoms, diagnose the likely cause step by step:
- Pages take 8-15 seconds to load
- Images appear broken or don't load
- Problem started yesterday after a content update
- Server CPU usage is normal
- Database queries are responding normally

Work through the diagnostic process systematically."
```

**💡 Pro Tip:** For complex reasoning tasks, you can enhance chain-of-thought by asking the model to consider alternative approaches: "Show your reasoning step by step, then double-check your work using a different method."

### 3.4 Role-Based Prompting

Role-based prompting involves assigning the AI a specific persona, profession, or expertise area to leverage specialized knowledge and communication styles. This technique taps into the model's training on domain-specific content and can dramatically improve the relevance and quality of responses.[^6][^7]

**Benefits of Role-Based Prompting:**

- Access to specialized knowledge and vocabulary
- Appropriate tone and communication style for the domain
- Context-aware responses that consider professional standards
- Enhanced credibility and depth in expert topics

**Effective Role Assignment Structure:**

```
"You are a [specific role] with [relevant experience/credentials]. 
[Context about the situation/audience].
[Specific task or question].
[Any constraints or requirements]."
```

**📝 Try This - Role-Based Examples:**

**Example 1: Technical Expert**

```
Prompt: "You are a senior cybersecurity analyst with 10 years of experience in enterprise security. A small business owner asks you to explain why they need cybersecurity measures, but they have limited technical knowledge and a tight budget. 

Explain the top 3 cybersecurity priorities for their business in simple terms, focusing on cost-effective solutions."
```

**Example 2: Creative Professional**

```
Prompt: "You are an experienced marketing copywriter who specializes in email campaigns for e-commerce businesses. Your writing style is conversational but persuasive, and you always focus on customer benefits rather than features.

Write a promotional email subject line and opening paragraph for a 25% off sale on athletic wear, targeting busy professionals who value both style and performance."
```

**Example 3: Domain Expert**

```
Prompt: "You are a certified financial planner with expertise in retirement planning. A 35-year-old client who earns $75,000 annually has just started thinking about retirement savings. They have no existing retirement accounts but can save $300 per month.

Provide a straightforward, actionable retirement planning strategy in terms they can easily understand."
```

**Advanced Role-Based Techniques:**

**1. Multi-Persona Dialogue**

```
Prompt: "Simulate a discussion between a data scientist and a marketing manager about implementing a customer segmentation project. The data scientist should focus on technical feasibility, while the marketing manager should focus on business impact and practical implementation."
```

**2. Role with Specific Constraints**

```
Prompt: "You are a pediatrician explaining a medical condition to parents. Use language appropriate for someone with a high school education, avoid medical jargon, and focus on practical next steps. Be reassuring but honest about the situation."
```

**3. Evolving Expertise**

```
Prompt: "You are a software engineering mentor working with a junior developer. Adjust your explanations based on their questions - start with basic concepts but be ready to dive deeper if they show understanding."
```

**💡 Pro Tip:** Research real professionals in your target domain to understand their typical concerns, language, and priorities. This will help you create more authentic and effective role-based prompts.

**Role Selection Guidelines:**

- **Specific roles work better than general ones**: "pediatric nurse" vs. "healthcare worker"
- **Include relevant experience level**: "senior," "junior," "certified," "experienced"
- **Consider the audience**: match the role's communication style to your intended audience
- **Add relevant constraints**: budget limitations, time constraints, regulatory requirements

***

This comprehensive foundation in core concepts and fundamental techniques provides the essential building blocks for effective prompt engineering. In the next sections, we'll explore advanced techniques and practical applications that build upon these fundamentals.
<span style="display:none">[^18][^19][^20][^21][^22][^23][^24][^25][^26][^27][^28][^29][^30][^31][^32][^33][^34][^35][^36][^37][^38][^39][^40][^41][^42][^43][^44][^45][^46][^47][^48][^49][^50][^51][^52]</span>


[^1]: https://kanerika.com/blogs/ai-prompt-engineering-best-practices/

[^2]: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api

[^3]: https://www.v7labs.com/blog/prompt-engineering-guide

[^4]: https://platform.openai.com/docs/guides/prompt-engineering

[^5]: https://www.anthropic.com/news/prompt-engineering-for-business-performance

[^6]: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview

[^7]: https://aws.amazon.com/blogs/machine-learning/prompt-engineering-techniques-and-best-practices-learn-by-doing-with-anthropics-claude-3-on-amazon-bedrock/

[^8]: https://promptsty.com/prompts-for-ethical-ai-development/

[^9]: https://shelf.io/blog/zero-shot-and-few-shot-prompting/

[^10]: https://www.promptingguide.ai/techniques/zeroshot

[^11]: https://www.promptingguide.ai/techniques/fewshot

[^12]: https://www.openxcell.com/blog/chain-of-thought-prompting/

[^13]: https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Zero-Shot-Prompting

[^14]: https://learnprompting.org/docs/advanced/zero_shot/introduction

[^15]: https://addlly.ai/blog/few-shot-prompting/

[^16]: https://www.geeksforgeeks.org/artificial-intelligence/few-shot-prompting/

[^17]: https://www.superannotate.com/blog/chain-of-thought-cot-prompting

[^18]: https://www.dhiwise.com/post/anthropic-prompt-engineering-techniques-for-better-results

[^19]: https://arize.com/course/prompt-optimization/

[^20]: https://www.pluralsight.com/courses/anthropic-prompt-engineering-best-practices

[^21]: https://www.promptingguide.ai/guides/optimizing-prompts

[^22]: https://platform.openai.com/docs/guides/prompt-engineering/strategy-write-clear-instructions

[^23]: https://cameronrwolfe.substack.com/p/automatic-prompt-optimization

[^24]: https://www.promptingguide.ai/introduction

[^25]: https://aclanthology.org/2024.acl-long.176/

[^26]: https://kjronline.org/DOIx.php?id=10.3348%2Fkjr.2024.0695

[^27]: https://clickup.com/blog/chain-of-thought-prompting/

[^28]: https://www.promptingguide.ai/techniques/cot

[^29]: https://relevanceai.com/prompt-engineering/master-few-shot-prompting-to-improve-ai-performance

[^30]: https://springsapps.com/knowledge/prompt-engineering-examples-and-best-practices

[^31]: https://www.deepchecks.com/question/common-metrics-evaluating-prompts/

[^32]: https://promptsty.com/prompts-for-artificial-intelligence-ethics/

[^33]: https://mirascope.com/blog/prompt-evaluation

[^34]: https://www.news.aakashg.com/p/prompt-engineering

[^35]: https://responsibleai.founderz.com/toolkit/principle_responsible_prompting

[^36]: https://portkey.ai/blog/evaluating-prompt-effectiveness-key-metrics-and-tools/

[^37]: https://www.digitalocean.com/resources/articles/prompt-engineering-best-practices

[^38]: https://www.codesmith.io/blog/llm-evaluation-guide

[^39]: https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-templates-and-examples.html

[^40]: https://www.godofprompt.ai/blog/is-a-b-testing-worth-it-for-ai

[^41]: https://promptlearnings.com/guide-to-iterative-prompt-testing/

[^42]: https://python.langchain.com/docs/concepts/prompt_templates/

[^43]: https://blog.promptlayer.com/you-should-be-a-b-testing-your-prompts/

[^44]: https://checklistgenerator.ai/checklists/a-checklist-for-a-prompt-engineer-f22993621c

[^45]: https://mirascope.com/blog/llm-prompt

[^46]: https://www.taskade.com/prompts/marketing/ab-testing-prompts

[^47]: https://promptdrive.ai/how-to-organize-ai-prompt-workflows/

[^48]: https://www.promptingguide.ai/introduction/examples

[^49]: https://langfuse.com/docs/prompts/a-b-testing

[^50]: https://www.linkedin.com/pulse/master-art-prompt-engineering-comprehensive-checklist-elodie-crespel-rw7se

[^51]: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/prompt-templates

[^52]: https://www.reddit.com/r/PromptEngineering/comments/1l77bam/prompt_engineering_iteration_whats_your_workflow/

