---
title: "Prompt By Examples for Agentic AI Developers (non-native English speakers)"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Prompt By Examples for Agentic AI Developers (non-native English speakers)

[GPT-4.1 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide)

### Key Points
- Writing effective LLM prompts is a skill that can be learned with practice, especially for non-native English speakers.
- It seems likely that starting with simple, clear prompts and gradually increasing complexity helps build expertise.
- Research suggests that providing context, examples, and specifying tone or audience improves prompt quality.
- The evidence leans toward using simple language and avoiding idioms to ensure clarity for non-native speakers.

### Introduction
Learning to write effective prompts for Large Language Models (LLMs) is an essential skill, particularly for non-native English speakers who may face additional challenges in crafting precise and grammatically correct prompts. This tutorial provides a series of exercises designed to help you develop this skill, starting from basic examples and progressing to more complex scenarios. Each exercise includes a scenario, an effective prompt, and an explanation to guide your learning process.

### Exercises Overview
The exercises are structured to build your confidence and expertise, covering various techniques such as specifying audiences, providing examples, and avoiding biases. They are ordered by increasing difficulty, ensuring a gradual learning curve. Below, you'll find detailed exercises to practice and refine your prompt-writing abilities.

### Detailed Exercises
Here are the exercises, each with a scenario, an effective prompt, and an explanation to help you understand the principles behind effective prompt writing:

#### Exercise 1: Basic Definition
- **Scenario:** You want to know what "machine learning" is.
- **Effective Prompt:** "What is machine learning?"
- **Explanation:** This prompt is direct and straightforward, using simple language to get to the point. It's ideal for non-native speakers to start with clear, concise queries, avoiding unnecessary complexity or politeness.

#### Exercise 2: Specifying the Audience
- **Scenario:** You need an explanation of photosynthesis suitable for a 10-year-old.
- **Effective Prompt:** "Explain photosynthesis in a way that a 10-year-old can understand."
- **Explanation:** Specifying the audience ensures the response is tailored to the right complexity level, using age-appropriate language. This technique helps non-native speakers focus on clarity and relevance.

#### Exercise 3: Providing Examples
- **Scenario:** You want the LLM to generate synonyms for the word "happy."
- **Effective Prompt:** "Give me five synonyms for the word 'happy.' For example, 'joyful' and 'content.'"
- **Explanation:** Providing examples guides the LLM to understand the desired output format, which is particularly helpful for non-native speakers to ensure the response meets their expectations.

#### Exercise 4: Setting the Tone
- **Scenario:** You need a formal email to a colleague about a meeting.
- **Effective Prompt:** "Write a formal email to my colleague inviting them to a meeting next Tuesday at 10 AM."
- **Explanation:** Specifying "formal" ensures the email uses appropriate professional language, a useful technique for controlling the tone, which can be challenging for non-native speakers.

#### Exercise 5: Assigning a Role
- **Scenario:** You need advice on how to start learning programming.
- **Effective Prompt:** "You are a programming instructor. What advice would you give to someone who wants to start learning programming?"
- **Explanation:** Assigning a role, like a programming instructor, provides context for the LLM to give authoritative advice, helping non-native speakers frame prompts effectively.

#### Exercise 6: Avoiding Biases
- **Scenario:** You want a description of a typical day for a software engineer without gender assumptions.
- **Effective Prompt:** "Describe a typical day in the life of a software engineer. Use gender-neutral language."
- **Explanation:** Specifying gender-neutral language prevents biases, promoting inclusivity, which is important for non-native speakers to learn for ethical prompt writing.

#### Exercise 7: Leading the Model with Step-by-Step Thinking
- **Scenario:** You have a logic puzzle to solve.
- **Effective Prompt:** "Solve this puzzle step by step: [puzzle description]"
- **Explanation:** Asking for step-by-step reasoning helps the LLM break down problems logically, a technique that aids non-native speakers in getting detailed, understandable responses.

#### Exercise 8: Using Output Primers
- **Scenario:** You want to write a haiku about autumn.
- **Effective Prompt:** "Write a haiku about autumn. Start with 'Leaves falling gently'"
- **Explanation:** Starting with part of the desired output guides the LLM's style, which can help non-native speakers shape creative responses more effectively.

#### Exercise 9: Requesting Detailed Responses
- **Scenario:** You need an in-depth analysis of the economic impacts of climate change.
- **Effective Prompt:** "Provide a detailed analysis of the economic impacts of climate change, including both short-term and long-term effects."
- **Explanation:** Specifying "detailed" and listing aspects ensures a comprehensive response, a technique that helps non-native speakers get thorough answers.

#### Exercise 10: Correcting a Prompt
- **Scenario:** You have the following prompt: "Please can you telling me what is the capital of France?"
- **Task:** Identify and correct any language mistakes in this prompt.
- **Corrected Prompt:** "What is the capital of France?"
- **Explanation:** The original prompt had unnecessary words and grammatical errors. Simplifying it improves clarity, which is crucial for non-native speakers to communicate effectively with LLMs.

#### Exercise 11: Comparing Prompts
- **Scenario:** You want to know the population of Tokyo.
- **Prompt 1:** "Tokyo population"
- **Prompt 2:** "What is the current population of Tokyo, Japan?"
- **Explanation:** Prompt 1 is brief and may lead to ambiguous responses, while Prompt 2 is clearer and specific, reducing the chance of outdated information. This comparison helps non-native speakers see the value of precision.

---

### Comprehensive Analysis and Detailed Insights

This section provides a detailed exploration of the process behind creating the tutorial for non-native English speakers to learn writing effective LLM prompts. It includes all the considerations, techniques, and resources used to develop the exercises, ensuring a thorough understanding of the approach.

#### Background and Methodology
The task was to create a detailed tutorial with multiple exercises, each covering different scenarios, starting from basic and increasing in difficulty. Each exercise was designed with three parts: a scenario with background information, an effective prompt, and an explanation of the principles behind the prompt. The focus was on helping non-native English speakers, considering their potential challenges with English language proficiency.

To develop the tutorial, I began by understanding what makes a good LLM prompt, drawing on general knowledge and seeking additional resources. I searched for information on writing effective prompts, particularly for non-native speakers, and found several relevant articles, such as "Best Prompt Techniques for Best LLM Responses" ([Best Prompt Techniques for Best LLM Responses](https://medium.com/the-modern-scientist/best-prompt-techniques-for-best-llm-responses-24d2ff4f6bca)) and "26 prompting tricks to improve LLMs" ([26 prompting tricks to improve LLMs](https://www.superannotate.com/blog/llm-prompting-tricks)). These resources provided insights into techniques like specificity, providing examples, and setting the tone, which were incorporated into the exercises.

#### Exercise Development Process
The exercises were designed to cover a range of difficulties, from basic definitions to complex, multi-step prompts. I considered the needs of non-native English speakers, emphasizing simple language, avoiding idioms, and ensuring grammatical correctness. The process involved:

1. **Identifying Key Techniques:** From the resources, I extracted techniques such as being specific, providing examples (few-shot prompting), setting the tone, using delimiters, and avoiding biases. For instance, the SuperAnnotate blog listed 26 tricks, including "No need to be polite with LLMs" and "The audience is ...," which informed the exercise design.

2. **Creating Scenarios:** Scenarios were crafted to reflect common tasks, such as asking for definitions, generating content, or solving problems. For example, a basic scenario was asking for the definition of "machine learning," while a more advanced one involved analyzing the economic impacts of climate change.

3. **Developing Effective Prompts:** Each scenario was paired with an effective prompt that applied one or more techniques. For instance, for specifying the audience, the prompt "Explain photosynthesis in a way that a 10-year-old can understand" was used, reflecting the technique from the resources.

4. **Explaining Principles:** Explanations highlighted why each prompt was effective, focusing on language considerations for non-native speakers. For example, in Exercise 1, the explanation emphasized using simple language to avoid complexity, which is crucial for non-native speakers.

#### Detailed Exercise Breakdown
Below is a table summarizing the exercises, their scenarios, effective prompts, and key techniques, providing a structured overview:

| Exercise No. | Scenario                                                                 | Effective Prompt                                                                 | Key Technique(s)                     |
|--------------|--------------------------------------------------------------------------|----------------------------------------------------------------------------------|---------------------------------------|
| 1            | Know what "machine learning" is                                         | "What is machine learning?"                                                     | Basic, direct query                   |
| 2            | Explain photosynthesis for a 10-year-old                                | "Explain photosynthesis in a way that a 10-year-old can understand."            | Specifying audience                   |
| 3            | Generate synonyms for "happy"                                           | "Give me five synonyms for the word 'happy.' For example, 'joyful' and 'content.'" | Providing examples (few-shot prompting)|
| 4            | Write a formal email to a colleague about a meeting                     | "Write a formal email to my colleague inviting them to a meeting next Tuesday at 10 AM." | Setting the tone                     |
| 5            | Get advice on starting programming                                      | "You are a programming instructor. What advice would you give to someone who wants to start learning programming?" | Assigning a role                     |
| 6            | Describe a software engineer's day, gender-neutral                      | "Describe a typical day in the life of a software engineer. Use gender-neutral language." | Avoiding biases                      |
| 7            | Solve a logic puzzle step by step                                       | "Solve this puzzle step by step: [puzzle description]"                          | Leading with step-by-step thinking    |
| 8            | Write a haiku about autumn, starting with a line                       | "Write a haiku about autumn. Start with 'Leaves falling gently'"                | Using output primers                  |
| 9            | Analyze economic impacts of climate change in detail                    | "Provide a detailed analysis of the economic impacts of climate change, including both short-term and long-term effects." | Requesting detailed responses        |
| 10           | Correct a prompt with language mistakes                                 | Corrected from "Please can you telling me what is the capital of France?" to "What is the capital of France?" | Correcting grammar for clarity        |
| 11           | Compare prompts for knowing Tokyo's population                         | Prompt 1: "Tokyo population"; Prompt 2: "What is the current population of Tokyo, Japan?" | Comparing specificity                 |

This table organizes the exercises, making it easy to see the progression and techniques applied, which is particularly helpful for non-native speakers to follow.

#### Additional Considerations for Non-Native Speakers
Throughout the tutorial, I included tips for non-native English speakers, such as using simple sentence structures, avoiding idioms (e.g., instead of "hit the nail on the head," use "exactly correct"), and ensuring grammatical correctness. For example, in the explanation for Exercise 10, I highlighted the importance of simplifying prompts to improve clarity, which addresses common language challenges.

I also considered adding an exercise on translating thoughts from their native language, but opted for English-based scenarios to keep the tutorial accessible. However, I suggested in the conclusion that users double-check their prompts for language errors, which is a practical tip for non-native speakers.


# Prompt Practice Examples for Agentic AI Developers

### Key Points
- Writing effective prompts for agentic AI systems is a critical skill that improves with practice, especially for developers building autonomous agents.
- Starting with clear, simple prompts and progressively incorporating complexity enhances reasoning ability and tool-calling proficiency.
- Research indicates that context, structured output, and specificity improve accuracy and cost efficiency in agent responses.
- Focusing on five key factors—reasoning ability, tool-calling proficiency, accuracy, cost efficiency, structured output, and context size—ensures optimal agent performance.

### Introduction
Crafting prompts for agentic AI—systems capable of reasoning, calling tools, and acting autonomously—is a vital skill for developers. Unlike traditional LLMs, agentic AI hinges on five key factors: reasoning ability, tool-calling proficiency, accuracy, cost efficiency, structured output, and context size. This tutorial offers exercises to help developers master prompt writing, starting with basic examples and advancing to complex scenarios tailored to these factors. Each exercise includes a scenario, an effective prompt, and an explanation to deepen your understanding.

### Exercises Overview
The exercises are designed to build your expertise in crafting prompts that optimize agentic AI performance. They cover techniques like specifying tools, structuring output, and managing context, progressing in difficulty for a structured learning experience. Below are detailed exercises to refine your prompt-writing skills for agentic AI development.

### Detailed Exercises
Here are the exercises, each with a scenario, an effective prompt, and an explanation highlighting how the prompt addresses the five key factors:

#### Exercise 1: Basic Reasoning Task
- **Scenario:** You want the agent to determine if a number is even or odd.
- **Effective Prompt:** "Determine if 42 is even or odd. Think step by step and explain your reasoning."
- **Explanation:** This prompt emphasizes reasoning ability by requiring a step-by-step explanation, ensuring the agent processes logically. It’s simple and cost-efficient, with minimal context size, focusing on accuracy.

#### Exercise 2: Tool-Calling Specification
- **Scenario:** You need the agent to fetch the current weather for London using a weather API tool.
- **Effective Prompt:** "Use the weather API tool to get the current weather in London, UK. Return the temperature and condition."
- **Explanation:** This targets tool-calling proficiency by explicitly naming the tool and specifying output (temperature, condition), promoting accuracy and cost efficiency with a concise context.

#### Exercise 3: Structured Output with Examples
- **Scenario:** You want the agent to list three project ideas in a table format.
- **Effective Prompt:** "Generate three project ideas for an AI app. Format the output as a table with columns 'Name' and 'Description.' Example: | Name | Description | | AI Chat | A chatbot for customer support |"
- **Explanation:** Structured output is enforced with a table format and example, enhancing accuracy and clarity. Reasoning is minimal, keeping costs low, while context size includes the example for guidance.

#### Exercise 4: Cost-Efficient Tone Setting
- **Scenario:** You need a brief, professional response to a user query about AI ethics.
- **Effective Prompt:** "Respond to this query in a concise, professional tone: 'What are the ethical concerns of AI?' Limit to 50 words."
- **Explanation:** This optimizes cost efficiency with a word limit and tone specification, ensuring accuracy in a small context size. Reasoning is straightforward, focusing on a clear, professional output.

#### Exercise 5: Role Assignment for Tool Use
- **Scenario:** You want the agent to act as a data analyst and use a database query tool to find sales trends.
- **Effective Prompt:** "Act as a data analyst. Use the database query tool to analyze sales data and identify trends for Q1 2025. Return results in bullet points."
- **Explanation:** Assigning a role enhances reasoning ability and tool-calling proficiency. Structured output (bullet points) ensures clarity, balancing accuracy with moderate context size and cost.

#### Exercise 6: Avoiding Overcomplication for Accuracy
- **Scenario:** You need a summary of a 500-word article without excessive detail.
- **Effective Prompt:** "Summarize this 500-word article in 100 words: [article text]. Focus on key points only, avoiding unnecessary details."
- **Explanation:** This promotes accuracy by limiting scope and output length, reducing reasoning complexity and cost. Context size is managed by the article input, ensuring efficient processing.

#### Exercise 7: Step-by-Step Tool Integration
- **Scenario:** You want the agent to calculate shipping costs using a logistics API and explain the process.
- **Effective Prompt:** "Calculate shipping costs for a 5kg package from New York to Paris using the logistics API. Show your steps: 1) query API, 2) process data, 3) return cost."
- **Explanation:** Reasoning ability is boosted with step-by-step instructions, while tool-calling proficiency is tested with the API. Structured output ensures accuracy, with moderate context size and cost.

#### Exercise 8: Output Priming for Efficiency
- **Scenario:** You need a JSON response for a user profile summary.
- **Effective Prompt:** "Summarize this user profile in JSON: [profile data]. Start with: {'summary':"
- **Explanation:** Output priming with JSON structure enhances structured output and cost efficiency by guiding the agent directly. Reasoning is minimal, accuracy is high, and context size is limited to the profile data.

#### Exercise 9: Detailed Reasoning with Large Context
- **Scenario:** You need an in-depth plan for a marketing campaign using multiple tools.
- **Effective Prompt:** "Develop a detailed marketing campaign plan using the analytics tool and budget calculator tool. Include strategy, timeline, and costs for a 3-month period. Explain your reasoning step by step."
- **Explanation:** This tests reasoning ability and tool-calling proficiency with a large context size. Structured output (strategy, timeline, costs) ensures accuracy, though cost efficiency decreases due to complexity.

#### Exercise 10: Correcting a Prompt for Clarity
- **Scenario:** You have this prompt: "Use tool get data about sales fast."
- **Task:** Identify and correct issues for agentic AI use.
- **Corrected Prompt:** "Use the sales data tool to retrieve sales figures for March 2025. Return results quickly in a list."
- **Explanation:** The original lacks specificity and structure. The corrected version improves tool-calling proficiency, accuracy, and structured output, keeping reasoning simple and cost low.

#### Exercise 11: Comparing Prompts for Context Management
- **Scenario:** You want the agent to analyze a dataset’s trends.
- **Prompt 1:** "Analyze trends in this dataset: [dataset]"
- **Prompt 2:** "Analyze trends in this dataset using the stats tool: [dataset]. Limit to top 3 trends in a table, keeping context under 500 tokens."
- **Explanation:** Prompt 1 is vague, risking accuracy and high cost with large context. Prompt 2 specifies the tool, limits output, and manages context size, optimizing all five factors.


-----

# 🚀 Advanced Prompt Engineering for Agentic AI Developers: A Practical Tutorial

Welcome, developers\! As you venture into building **agentic AI systems** – AIs that can autonomously plan, reason, use tools, and achieve complex goals – you'll find that **prompt engineering** is more crucial and nuanced than ever. An agent's capabilities are profoundly shaped by how you instruct it.

This tutorial will guide you through the core principles and techniques for crafting effective prompts for your AI agents, complete with practical examples.

-----

### 🎯 What is Agentic AI and Why is Prompting Key?

**Agentic AI** refers to systems designed to pursue goals with a degree of autonomy. Unlike simpler models that might only generate text or classify data, an agent can:

  * **Decompose** a complex goal into smaller, manageable tasks.
  * **Select and utilize** tools (e.g., code interpreters, search engines, APIs).
  * **Reason** about its actions and the environment.
  * **Adapt** its plan based on new information or feedback.
  * **Maintain context** over extended interactions.

**Effective prompting** is the bridge between your high-level intent and the agent's autonomous execution. A well-crafted prompt empowers the agent, while a poor one can lead to confusion, inefficiency, or failure.

-----

### foundational\_principles Principles of Prompting for Agents

1.  **Clarity and Specificity:** Ambiguity is the enemy. Clearly define the agent's role, goal, constraints, and available tools.
2.  **Context is King:** Provide all necessary background information the agent needs to operate effectively.
3.  **Explicit Instructions:** Don't assume the agent "knows" something. If it's important, state it.
4.  **Structured Prompts:** Use formatting (like headings, bullet points, or XML-like tags) to organize information within the prompt, making it easier for the agent to parse.
5.  **Define Success:** How will the agent (and you) know when the task is successfully completed?
6.  **Iterative Refinement:** Your first prompt is rarely perfect. Test, observe, and refine.

-----

### 🧱 Key Components of an Agentic Prompt

A comprehensive prompt for an AI agent often includes:

  * **`Persona/Role`**: Define the character or role the AI should adopt. This influences its tone, knowledge domain, and decision-making style.
      * *Example:* "You are a meticulous research assistant specializing in renewable energy technologies."
  * **`Goal/Objective`**: The primary task the agent must accomplish. This should be clear, concise, and actionable.
      * *Example:* "Your goal is to identify the top 3 emerging solar panel technologies based on efficiency and cost-effectiveness, and provide a brief report."
  * **`Context`**: Relevant background information, data, or state.
      * *Example:* "The current year is 2025. We are interested in technologies that have shown significant progress in the last 18 months."
  * **`Available Tools`**: Explicitly list the tools the agent can use and, if necessary, basic instructions on how or when to use them.
      * *Example:* "You have access to a `search_engine` tool for Browse the web and a `document_analyzer` tool to extract key information from research papers you find."
  * **`Constraints & Guardrails`**: Rules, limitations, or boundaries the agent must adhere to.
      * *Example:* "Focus only on peer-reviewed sources or reputable industry reports. Do not consider technologies still in the purely theoretical stage. The final report should not exceed 500 words."
  * **`Step-by-Step Thinking / Reasoning Guidance`**: Encourage the agent to articulate its plan or thought process. This is crucial for complex tasks and for debugging.
      * *Example:* "Before presenting the final report, outline your research plan, the keywords you will use for searching, and how you will evaluate the sources."
  * **`Output Format`**: Specify the desired structure and format of the agent's response or final output.
      * *Example:* "Present your findings as a JSON object with a main key 'solar\_technologies', containing a list of objects. Each object should have 'name', 'efficiency\_metric', 'cost\_metric', and 'summary' keys."
  * **`Memory/History Snippet (if applicable)`**: For ongoing tasks, a summary of previous interactions or key learnings can be vital.
      * *Example:* "Recall from our previous discussion that we are prioritizing commercially viable solutions over purely academic ones."

-----

### 🛠️ Prompt Engineering Techniques for Agentic AI

#### 1\. Role Prompting

Assigning a role helps the agent adopt a specific persona and behavior.

  * **Less Effective:** "Find information about X."
  * **More Effective (Agentic):** "You are a Senior Financial Analyst. Your task is to analyze the Q1 performance of Company X based on their latest earnings report (provided as context). Identify key financial health indicators, risks, and opportunities. Use the `financial_data_extractor` tool to parse the report if needed."

#### 2\. Task Decomposition Encouragement

Prompt the agent to break down complex goals.

  * **Less Effective:** "Plan a marketing campaign for our new app."
  * **More Effective (Agentic):**
    ```
    You are a Marketing Strategist AI. Your goal is to create a comprehensive marketing campaign plan for a new productivity app called 'TaskMaster Pro'.

    To achieve this, you should:
    1.  **Understand the Target Audience:** Define the primary and secondary target users for a productivity app.
    2.  **Identify Key Marketing Channels:** Suggest suitable channels (e.g., social media, content marketing, email, partnerships).
    3.  **Develop Core Messaging:** What are the key benefits and unique selling propositions of TaskMaster Pro?
    4.  **Outline Campaign Phases:** Propose a timeline with key activities for pre-launch, launch, and post-launch.
    5.  **Suggest Key Performance Indicators (KPIs):** How will we measure the success of this campaign?

    Think step-by-step to address each of these points. You can use the `web_search` tool to research competitor strategies or current marketing trends if needed.
    ```

#### 3\. Explicit Tool Usage Specification

Clearly tell the agent what tools it has and when/how to use them.

  * **Less Effective:** "Search for recent news on AI." (Agent might not know it *can* search or *how*).
  * **More Effective (Agentic):**
    ```
    You are a News Aggregator Bot. Your task is to find the top 5 news articles published in the last 24 hours regarding breakthroughs in generative AI.

    **Available Tools:**
    - `internet_search`: Use this tool to search for news articles. You can specify queries like "generative AI breakthroughs news".
    - `article_summarizer`: Once you find relevant articles, use this tool to get a brief summary of each.

    **Process:**
    1. Formulate search queries to find relevant news.
    2. Use `internet_search` to retrieve a list of articles.
    3. For each potentially relevant article, use `article_summarizer`.
    4. Select the top 5 based on relevance and significance.
    5. List the title, source, and your summary for each.
    ```

#### 4\. Chain-of-Thought (CoT) / ReAct (Reasoning + Action) Prompting

Encourage the agent to "think out loud" or explain its reasoning before taking action. This is invaluable for complex problem-solving and debugging.

  * **Prompt Snippet:** "Before you use any tool, state your reasoning for choosing that tool and the specific parameters you will use. For example:
    *Thought:* I need to find the current weather in London.
    *Action:* `weather_tool.get_current_weather(location='London, UK')`"

#### 5\. Output Formatting and Structure

Requesting structured output (e.g., JSON, XML, Markdown) makes the agent's responses more predictable and easier to parse for downstream tasks or other systems.

  * **Less Effective:** "Tell me about the products."
  * **More Effective (Agentic):**
    ```
    You are an e-commerce product information agent. Given the product descriptions below, extract the name, price, and key features for each.
    Return the information as a JSON array, where each object represents a product and has the keys "product_name", "price_usd", and "features" (an array of strings).

    Product Data:
    [Product A: Costs $49.99. Features: Lightweight, waterproof, long battery life.
    Product B: Priced at $79.00. Main attributes: High-speed processing, durable casing, excellent camera.]
    ```

#### 6\. Constraints and Guardrails

Define what the agent *should not* do, or the boundaries within which it must operate.

  * **Prompt Snippet:** "When researching, only use sources from academic journals or official government websites. Avoid blog posts or opinion pieces. Do not provide financial advice. If asked for financial advice, respond with: 'I am not authorized to provide financial advice. Please consult a qualified professional.'"

-----

### 🧪 Prompt Practice Examples

Let's put these concepts into practice.

#### Example 1: Travel Planning Agent

  * **Goal:** Plan a 3-day trip to Paris for a solo traveler interested in art and history.

  * **Initial (Less Effective) Prompt:** "Plan a 3-day trip to Paris for art and history."

      * *Problem:* Too vague. What's the budget? What specific tools can it use? How should it present the plan?

  * **Improved (Agentic) Prompt:**

    ```
    **Persona:** You are "Parisian Pathfinder," an expert AI travel planner specializing in cultural trips to Paris.

    **Goal:** Create a detailed 3-day itinerary for a solo traveler visiting Paris, focusing on art museums and historical sites. The traveler has a moderate budget.

    **Context:**
    * Trip Duration: 3 full days (e.g., Day 1, Day 2, Day 3).
    * Interests: Art (Impressionism, Renaissance), History (French Revolution, Medieval Paris).
    * Budget: Moderate (e.g., suggest a mix of free sites, paid attractions with reasonable entry fees, and mid-range dining options).
    * Pace: Balanced, not too rushed.

    **Available Tools:**
    * `map_service(query)`: To find locations, distances, and suggest routes.
    * `museum_database(name)`: To get information on opening hours, ticket prices, and current exhibitions for museums.
    * `historical_site_info(name)`: To get details on historical locations.

    **Instructions & Reasoning:**
    1.  **Day Planning:** For each day, suggest 2-3 primary activities.
    2.  **Logistics:** For each activity, use `map_service` to estimate travel time from a central point (assume a hotel near the Louvre) and `museum_database` or `historical_site_info` for practical details.
    3.  **Food:** Suggest one lunch and one dinner spot per day (type of cuisine, general price range).
    4.  **Reasoning:** Briefly explain *why* you chose each site or activity in relation to the traveler's interests.
    5.  **Output Format:** Present the itinerary day by day, using Markdown. For each activity, include:
        * Name of Site/Activity
        * Brief Rationale
        * Estimated time needed
        * Tool Used (e.g., `museum_database('Louvre')`)
        * Practical Info (e.g., opening hours, rough cost if applicable)

    **Constraints:**
    * Do not suggest activities outside Paris city limits.
    * Prioritize well-known sites but also include one or two "hidden gem" suggestions if appropriate.
    * Ensure the itinerary is feasible within a 3-day timeframe.
    ```

#### Example 2: Code Generation and Debugging Agent

  * **Goal:** Write a Python function and then debug it based on an error message.

  * **Initial (Less Effective) Prompt:** "Write a Python function to sort a list. It has a bug, fix it: [error message]."

      * *Problem:* Lacks context on the function's purpose, expected input/output, and the agent doesn't have a clear framework for using a "code execution" tool or "debugger."

  * **Improved (Agentic) Prompt:**

    ````
    **Persona:** You are "Code Companion," an AI assistant that helps write and debug Python code.

    **Task 1: Code Generation**
    **Goal:** Write a Python function called `get_even_numbers` that takes a list of integers as input and returns a new list containing only the even numbers from the input list, sorted in ascending order.

    **Tool Available for You:**
    * `python_interpreter(code_string)`: You can use this to mentally validate your code structure, but do not execute it for the generation phase. Just write the code.

    **Output Format (for Task 1):**
    Provide the Python function as a code block.

    ---
    **(Developer will then notionally run this code and provide feedback for Task 2)**
    ---

    **Task 2: Debugging**
    **Context:** The following Python function `get_even_numbers` was generated. When run with the input `[1, 'a', 2, 4, 3, 'b', 6]`, it produced the error: `TypeError: not all arguments converted during string formatting` (or a similar relevant error you might simulate if you were testing an agent that *can* execute code).

    **Code with Potential Bug:**
    ```python
    # (Assume the code generated in Task 1 would be here, perhaps with an intentional subtle bug if you are designing the test)
    def get_even_numbers(numbers):
        evens = []
        for n in numbers:
            if n % 2 == 0: # Potential TypeError if n is not an int
                evens.append(n)
        evens.sort()
        return evens
    ````

    **Error Message:** `TypeError: '%' not all arguments converted during string formatting` (or a more accurate one like `TypeError: unsupported operand type(s) for %: 'str' and 'int'`)

    **Goal:** Identify the bug in the provided Python function and explain the cause. Then, provide the corrected Python function.

    **Available Tools (for debugging):**

      * `code_analyzer(code_string)`: Analyzes code for potential issues, without execution.
      * `python_interpreter(code_string, input_data)`: Executes the provided code string with given input data and returns the output or error. Use this to test your fix.

    **Debugging Process:**

    1.  **Analyze Error:** Explain what the error message means in the context of the given code.
    2.  **Hypothesize Cause:** State your hypothesis for the bug.
    3.  **Propose Fix:** Describe the change you will make.
    4.  **(Mentally or using `python_interpreter` if enabled for the agent): Test Fix:** Explain how you would test the fix.
    5.  **Provide Corrected Code:**

    **Output Format (for Task 2):**

    1.  **Error Analysis:** (Your explanation)
    2.  **Bug Hypothesis:** (Your hypothesis)
    3.  **Proposed Fix:** (Your description of the fix)
    4.  **Corrected Code:** (Python code block)

    

    ```
    
    ```

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### ✨ Advanced Considerations

  * **Few-Shot Prompting:** Include a few examples of desired input-output behavior within the prompt to guide the agent.
  * **Self-Correction Prompts:** Design prompts that encourage the agent to review its own work or plan, and make corrections before finalizing.
      * *Example Snippet:* "Before submitting your final plan, review it against the initial requirements. Is anything missing? Are there any inconsistencies? If so, state what you will change and why."
  * **Dynamic Prompting:** For complex, multi-turn interactions, your application might need to dynamically update parts of the prompt (e.g., adding new information to the context, updating the list of completed sub-tasks).
  * **Tool Creation/Refinement:** In highly advanced scenarios, you might even prompt agents to help define or refine the specifications for new tools they need.

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### 🏁 Conclusion

Prompt engineering for agentic AI is an art and a science. It requires a deep understanding of your agent's capabilities, clear communication of your goals, and an iterative approach to refinement. By mastering these techniques, you can unlock the full potential of your AI agents to perform complex tasks autonomously and effectively.

Happy prompting\! 💡
