add more prompt content

courses/theoretical_basics/prompt_engineering.md

@@ -16,14 +16,40 @@ subtitle: Prompt Engineering
   - [Bridging the Knowledge Gap](#bridging-the-knowledge-gap)
   - [Summary](#summary)
 - [Anatomy of an Effective Prompt](#anatomy-of-an-effective-prompt)
+  - [Key Traits of an Effective Prompt](#key-traits-of-an-effective-prompt)
+  - [Essential Components of a Prompt](#essential-components-of-a-prompt)
+  - [Examples of Effective Prompts](#examples-of-effective-prompts)
+  - [Common Mistakes to Avoid](#common-mistakes-to-avoid)
+  - [Refining Prompts: An Iterative Process](#refining-prompts-an-iterative-process)
+  - [Summary](#summary-1)
 - [Elements of a Prompt](#elements-of-a-prompt)
+  - [Instructions](#instructions)
+  - [Context](#context)
+  - [Output Format](#output-format)
+  - [Persona or Tone](#persona-or-tone)
+  - [Examples](#examples)
+  - [Constraints](#constraints)
+  - [Instructions for Iterative Refinement](#instructions-for-iterative-refinement)
+  - [Practical Example: Combining Elements](#practical-example-combining-elements)
+  - [Summary](#summary-2)
 - [Types of Prompting](#types-of-prompting)
+  - [Zero-Shot Prompting](#zero-shot-prompting)
+  - [One-Shot Prompting](#one-shot-prompting)
+  - [Few-Shot Prompting](#few-shot-prompting)
+  - [When to Use Each Type](#when-to-use-each-type)
+  - [Summary](#summary-3)
 - [Advanced Prompting Techniques](#advanced-prompting-techniques)
+  - [Summary](#summary-4)
 - [Iterative Prompt Refinement](#iterative-prompt-refinement)
+  - [Summary](#summary-5)
 - [Structuring Outputs](#structuring-outputs)
+  - [Summary](#summary-6)
 - [Handling Model Limitations](#handling-model-limitations)
+  - [Summary](#summary-7)
 - [Applications of Prompt Engineering](#applications-of-prompt-engineering)
+  - [Summary](#summary-8)
 - [Best Practices](#best-practices)
+  - [Summary](#summary-9)
 - [☝️ References](#️-references)
 
 
@@ -98,41 +124,291 @@ productive, and impactful.
 
 
 ## Anatomy of an Effective Prompt
-bla bla
+The effectiveness of a prompt determines the quality of responses generated by LLMs. Crafting an effective prompt is both an art and a science,
+involving clarity, precision, and structure. Below, we explore the key traits and components that form the anatomy of an effective prompt.
+
+### Key Traits of an Effective Prompt
+1. Clarity
+A clear prompt provides relevant context and avoids ambiguity. The model performs best when instructions are straightforward and to the point.
+- Ineffective Example: "Tell me about technology."
+- Effective Example: "Describe the recent advancements in artificial intelligence over the past five years."
+
+2. Specificity
+A specific prompt narrows the scope, ensuring the model's response is aligned with the user’s intent.
+- Ineffective Example: "Summarize this document."
+- Effective Example: "Summarize the given article in three bullet points, focusing on its research findings."
+
+3. Open-Endedness
+Open-ended prompts encourage the model to generate insightful and comprehensive outputs, especially for exploratory or creative tasks.
+- Ineffective Example: "List AI advantages."
+- Effective Example: "What are the potential advantages of using AI in public healthcare systems?"
+
+### Essential Components of a Prompt
+1. Instructions
+The instructions should explicitly define the task the model needs to perform. Use action verbs and avoid ambiguity.
+Example: "Provide a detailed explanation of the role of AI in radiological diagnostics."
+
+2. Context
+Including relevant background information ensures the model understands the subject better. This can include facts, data or scenarios.
+Example: "Given that AI-powered imaging can detect lung abnormalities early, discuss its potential to reduce mortality rates in lung cancer."
+
+3. Output Format
+Specifying the desired format enhances usability. Clearly state whether you expect a list, table, essay or structured response.
+Example: "Summarize the article in three bullet points, highlighting key findings."
+
+4. Persona or Tone
+Indicate the tone or perspective you want the model to adopt.
+Example: "Explain the basics of machine learning to a 12-year-old in a simple and engaging manner."
+
+### Examples of Effective Prompts
+1. For Information Retrieval
+Prompt: "What are the key causes and potential solutions for antibiotic resistance? Provide your answer in three concise paragraphs."
+
+2. For Creative Writing
+Prompt: "Write a short story about a doctor discovering a new treatment for an ancient disease, using a futuristic AI device."
+
+3. For Data Summarization
+Prompt: "Summarize the data trends from the following text, focusing on detection rates and chances of cure."
+
+### Common Mistakes to Avoid
+1. Ambiguity
+Vague prompts often lead to generalized or irrelevant responses.
+- Avoid: "Explain AI."
+- Use: "Explain how AI is being applied to improve patient outcomes in interventional radiology."
+
+2. Overloading
+Including too much irrelevant information can confuse the model.
+- Avoid: "Using data from 20 studies across 10 years, summarize findings related to AI's healthcare impact."
+- Use: "Summarize AI's impact on the healthcare system based on the latest trends in research."
+
+1. Unnecessary Complexity
+Overly technical or convoluted phrasing can hinder comprehension.
+- Avoid: "Evaluate the ramifications of AI in cross-sectoral clinical applications, particularly focusing on symbiotic integrations."
+- Use: "Explain how AI is transforming clinical processes across multiple sectors."
+
+### Refining Prompts: An Iterative Process
+No prompt is perfect the first time. Improving a prompt often involves iterative refinement:
+- Step 1: Test the prompt and review the response.
+- Step 2: Adjust wording, context, or format to address shortcomings.
+- Step 3: Reiterate until the desired outcome is achieved.
+
+For example:
+- Initial Prompt: "Write about machine learning."
+- Refined Prompt: "Write an introductory paragraph about the key concepts of supervised and unsupervised machine learning."
+
+### Summary
+Crafting an effective prompt is fundamental to maximizing the utility of LLMs. By focusing on clarity, specificity, and structure,
+users can guide models to produce accurate, relevant, and creative responses. Whether you're summarizing data, generating insights,
+or exploring creative ideas, the anatomy of an effective prompt is the blueprint for success.
 
-**Summary:**
 - Clarity: Reduces ambiguity by providing clear context.
 - Specificity: Targets the exact requirement to avoid irrelevant information.
-- Open-Endedness: Encourages detailed and insightful outputs​(prompt1)​(prompt2).
+- Open-Endedness: Encourages detailed and insightful outputs.
 
-## Elements of a Prompt
-bla bla
 
-**Summary:**
+## Elements of a Prompt
+An effective prompt is a well-structured input that guides a LLM to deliver precise and relevant responses.
+Crafting such prompts involves understanding and implementing key elements that make the prompt clear, contextual, and actionable.
+Below, we explore the essential elements of a prompt, illustrating their significance and impact on the output.
+
+### Instructions
+The instructions define what the model is expected to do. They are the backbone of the prompt and should:
+- Use clear and specific action verbs such as "summarize," "explain," "analyze," or "list."
+- Avoid vague or ambiguous terms that could lead to irrelevant outputs.
+- Examples:
+  - Ineffective: "Talk about healthcare."
+  - Effective: "Provide an overview of the challenges facing public healthcare systems in rural areas."
+
+### Context
+Providing the right context helps the model understand the task better, enabling more accurate responses. Context may include
+background information, scenarios, or constraints relevant to the task.
+
+Examples:
+- With Context: "Given that AI tools are increasingly used in radiology to detect anomalies in imaging, discuss their
+  potential impact on patient outcomes."
+- Without Context: "Discuss AI's impact on healthcare."
+
+Including context ensures that the response is targeted and meaningful, especially for specialized topics.
+
+### Output Format
+Specifying the desired output format improves the usability of the response. Formats can range from lists and bullet points to
+structured tables or essays, depending on the task.
+
+Examples:
+- List: "List three advantages of telemedicine in rural healthcare."
+- Table: "Create a table comparing the pros and cons of manual and automated medical diagnoses."
+- Essay: "Write an essay discussing the ethical considerations of using AI in mental health diagnostics."
+
+By pre-defining the format, you reduce ambiguity and ensure the output matches your needs.
+
+### Persona or Tone
+The persona or tone directs the style and voice of the response, tailoring it to a specific audience or purpose. This can make
+the content more relatable, professional, or engaging, depending on the requirement.
+
+Examples:
+- Formal Tone: "Explain the importance of cybersecurity in healthcare in a professional tone suitable for a medical journal."
+- Casual Tone: "Describe cybersecurity for healthcare professionals in simple terms suitable for a casual blog post."
+- Persona-Specific: "Answer as if you are a medical ethics expert explaining privacy concerns in AI applications."
+
+Adopting the right tone or persona ensures the response is aligned with the intended audience.
+
+### Examples
+Providing examples within the prompt helps the model understand the task and align its output with your expectations.
+This is especially useful for creative or technical tasks.
+
+Examples:
+- Technical Task: "Classify the following statements into 'positive' or 'negative':
+  - 'This medication improved my condition significantly.'
+  - 'The side effects were unbearable.'
+  - Format the response as:
+    - Statement 1: Positive
+    - Statement 2: Negative."
+
+Examples set a clear precedent for the model, reducing errors and ensuring the output meets expectations.
+
+### Constraints
+Constraints establish boundaries for the model’s response, such as word count, sentence count, or thematic restrictions.
+These help focus the output and ensure it is concise and relevant.
+
+Examples:
+- "Summarize this article in no more than 100 words."
+- "Write a response in 3-5 sentences explaining the benefits of exercise for mental health."
+
+### Instructions for Iterative Refinement
+Some tasks require responses to go through multiple stages of improvement. Including instructions for iterative
+refinement allows the model to progressively refine its output.
+
+Examples:
+- "Generate a list of potential public health initiatives to reduce childhood obesity. Then refine the list to include
+  only those that can be implemented within a year."
+- "Provide an initial draft for a patient education leaflet on diabetes management. Then revise it to include simple
+  language suitable for non-medical readers."
+
+### Practical Example: Combining Elements
+Here’s an example of a comprehensive prompt that incorporates all elements:
+
+Prompt:
+"Write a formal report for healthcare policymakers discussing the benefits and challenges of using AI in emergency medicine.
+Begin with an introduction, then provide a balanced discussion with at least three points for each side. Conclude with a summary
+and recommendations. Use bullet points for the main arguments. Limit the response to 300 words."
+
+This prompt includes:
+- Instructions: Write a formal report.
+- Context: The topic is AI in emergency medicine for healthcare policymakers.
+- Output Format: Structured with bullet points and word count limits.
+- Tone: Formal, tailored for policymakers.
+- Constraints: Word limit of 300 words.
+
+### Summary 
 - Instructions: Define the task clearly (e.g., summarize, translate).
 - Context: Provide necessary background for accurate responses.
 - Output Format: Specify desired output structure (e.g., lists, tables).
-- Persona: Tailor responses to emulate specific roles or tones (e.g., formal, technical)​(prompt2)​(prompts_for_developers3).
+- Persona: Tailor responses to emulate specific roles or tones.
+
+Understanding and applying the key elements of a prompt is essential for unlocking the full potential of LLMs.
+By focusing on instructions, context, format, tone, examples, and constraints, you can craft prompts that guide the model effectively,
+ensuring outputs are accurate, relevant, and tailored to your needs. A thoughtful approach to prompt creation not only improves the
+immediate response but also builds a foundation for iterative refinement and long-term success in working with AI.
 
 ## Types of Prompting
-bla bla
+Prompting refers to the techniques used to interact with LLMs by providing input instructions. These techniques influence how the
+model interprets and responds to a query. Depending on the level of guidance provided, prompting can be classified into zero-shot,
+one-shot, and few-shot prompting. Each type serves specific purposes, offering flexibility to address simple to complex tasks.
+
+### Zero-Shot Prompting
+**Definition:**
+Zero-shot prompting involves giving the model a task without any examples or demonstrations. It relies solely on the model’s
+pre-trained knowledge and understanding of the language.
+
+**Use Case:**
+- Ideal for straightforward tasks where context is minimal or general knowledge suffices.
+
+**Examples:**
+- Prompt: "What is the capital of Germany?"
+- Output: "The capital of Germany is Berlin."
+
+- Prompt: "Explain the importance of hand hygiene in preventing infections."
+- Output: "Hand hygiene is critical in preventing infections because it removes germs that could otherwise be transmitted
+  to others or cause self-infection."
+
+**Advantages:**
+- Simple and quick.
+- Requires no additional context or examples.
+
+**Challenges:**
+- May produce generalized or vague responses for complex tasks.
+
+### One-Shot Prompting
+**Definition:**
+In one-shot prompting, the model is provided with a single example to guide its understanding of the task.
+This example acts as a template for generating similar responses.
+
+**Use Case:**
+Useful when you want to establish a clear format or style for the response.
+
+**Examples:**
+- Prompt: "Provide an analysis of a historical event. For example:
+    - Event: Moon Landing (1969)
+    - Analysis: The Apollo 11 mission demonstrated the technological advancements of the United - States during
+      the Cold War era and symbolized human achievement in space exploration.
+    - Event: The fall of the Berlin Wall (1989)."
+- Output: "The fall of the Berlin Wall marked the end of the Cold War, symbolizing the collapse of communist
+  regimes in Eastern Europe and the triumph of democratic ideals."
+
+**Advantages:**
+- Helps clarify format and style.
+- Improves task-specific accuracy.
+
+**Challenges:**
+- Limited guidance may not suffice for highly nuanced tasks.
+
+### Few-Shot Prompting
+**Definition:**
+Few-shot prompting provides the model with multiple examples of a task, offering a broader context for understanding
+and generating responses. This method is powerful for more complex or domain-specific tasks.
+
+**Use Case:**
+Ideal for nuanced tasks requiring context or pattern recognition.
+
+**Examples:**
+- Prompt: "Classify the following texts into categories based on content. Examples:
+  - 'COVID-19 vaccines are effective in reducing severe illness.' → Category: Healthcare
+  - 'The latest iPhone model features a dynamic display.' → Category: Technology
+  - 'Interest rates have been hiked by 0.5% this quarter.' → Category: Finance
+  - Text: 'NASA successfully landed a rover on Mars.'"
+- Output: "Category: Space Exploration."
+- 
+**Advantages:**
+- Enhances accuracy by showcasing patterns or examples.
+- Suitable for contextual and detailed tasks.
+
+**Challenges:**
+- Can become verbose, especially for large datasets.
+- Requires thoughtful selection of examples to avoid bias.
+
+### When to Use Each Type
+- Zero-Shot: When simplicity and speed are critical. Examples include factual queries or straightforward definitions.
+- One-Shot: When output formatting is important, such as generating reports or lists in a specific style.
+- Few-Shot: For tasks involving sentiment analysis, classification, or domain-specific knowledge, where examples
+  enhance the model’s accuracy.
+
+### Summary
+Understanding the different types of prompting—zero-shot, one-shot, and few-shot—allows users to tailor their interactions
+with LLMs for optimal results. Each method has its strengths and limitations, making them suitable for varying levels of
+task complexity and specificity. By leveraging the right type of prompting, users can ensure accurate, relevant and efficient
+outputs, unlocking the full potential of language models.
+
+| Type              | Examples Provided |             Best For            |              Advantages              |             Challenges             |
+| :---------------- | :---------------: | :-----------------------------: | :----------------------------------: | :--------------------------------: |
+| Zero-Shot         |        None       | Simple, general tasks           | Quick and easy                       | May lack depth for complex tasks   |
+| One-Shot          |        One        | Tasks needing a format/template | Establishes format and clarity       | Limited context                    |
+| Few-Shot          |      Multiple     | Complex or nuanced tasks        | Captures patterns, improves accuracy | Requires careful example selection |
 
-**Summary:**
-1. Zero-Shot Prompting
-  - No examples provided.
-  - Relies solely on the model's pre-trained knowledge.
-  - Example: "Explain what AI is."
-2. One-Shot Prompting
-  - Includes a single example to guide the model.
-  - Example: "Q: What is 2+2? A: 4. Q: What is 3+5? A:"
-3. Few-Shot Prompting
-  - Provides multiple examples for more complex tasks.
-  - Example: Sentiment classification with annotated examples​(prompt3)​(prompts_for_developers2).
 
 ## Advanced Prompting Techniques
 bla bla
 
-**Summary:**
+### Summary
 1. Chain-of-Thought (CoT) Prompting
   - Encourages step-by-step reasoning before answering.
   - Example: "Let’s think step by step: [detailed reasoning]."
@@ -145,7 +421,7 @@ bla bla
 ## Iterative Prompt Refinement
 bla bla
 
-**Summary:**
+### Summary
 Prompts are rarely perfect initially. Refinement involves:
 - Building a basic prompt.
 - Analyzing the output.
@@ -155,31 +431,31 @@ Prompts are rarely perfect initially. Refinement involves:
 ## Structuring Outputs
 bla bla
 
-**Summary:**
+### Summary
 - Tables: Explicitly mention expected columns and rows.
 - Lists: Specify ordered or unordered formats.
 - Structured Paragraphs: Include headings and subheadings for clarity​(prompts_for_developers1)​(prompts_for_developers3).
 
 ## Handling Model Limitations
 bla bla
 
-**Summary:**
+### Summary
 - Biases: Reflect societal stereotypes or training data biases.
 - Hallucinations: Confidently presenting incorrect facts.
 - Overfitting: Poor generalization beyond training data​(prompt3).
 
 ## Applications of Prompt Engineering
 bla bla
 
-**Summary:**
+### Summary
 - Text Summarization: Condenses lengthy texts into concise outputs.
 - Text Transformation: Adjusts tone, style, or translates content.
 - Chatbot Development: Guides chatbots to respond appropriately​(prompts_for_developers3)​(prompts_for_developers4).
 
 ## Best Practices
 bla bla
 
-**Summary:**
+### Summary
 - Use delimiters (--- or """) to separate sections in prompts.
 - Specify constraints (e.g., word count, style) for outputs.
 - Test prompts with varied examples to ensure robustness​(prompts_for_developers2)​(prompts_for_developers3).