Mastering Retrieval-Augmented Generation (RAG) and Prompt Design 

Below you will find some suggested materials on these topics, which we believe may help you complete the final activity in the lesson. Please note that using these resources is entirely optional, and you are welcome to explore any other sources or courses at your own discretion.

Introduction (2 Minutes)

Opening Statement:

• Welcome to this session on Principles of Effective Prompt Design. In today’s lecture, we will explore how to craft prompts that maximize the performance of Large Language Models (LLMs) by leveraging context and structuring inputs effectively.

Agenda:

1. 1. Principles of effective prompt design.

   2. Using retrieved context to enhance LLM performance.

   3. Examples of prompts for various tasks, such as summarization and question answering.

Part 1: Principles of Effective Prompt Design (5 Minutes)

Why is Prompt Design Important?

• • • LLMs generate outputs based on the prompts they receive. A well-constructed prompt provides clear instructions and relevant context, resulting in accurate and relevant responses.

    • Poorly designed prompts can lead to vague or irrelevant outputs, wasting computational resources and time.

Key Principles of Prompt Design:

1. Clarity and Specificity:

   • Avoid ambiguity. Be explicit about the task, format, and expected outcome.

   • Example:

     • Poor Prompt: "Summarize this."

     • Effective Prompt: "Summarize the following text into three concise bullet points focusing on key insights."

2. Provide Context:

   • Context enables LLMs to understand the query’s relevance. Use retrieved information or additional details.

   • Example:

     • Without Context: "Who can help me with Python?"

     • With Context: "From the retrieved profiles, who are the Python experts?"

3. Task-Specific Instructions:

   • Tailor the prompt to the task, whether it’s summarization, classification, or translation.

   • Example:

     • Summarization: "Summarize this text in under 50 words."

     • Classification: "Classify the following text as positive, negative, or neutral."

4. Set Constraints:

   • Specify output length, tone, or style.

   • Example: "Respond in a professional tone with no more than three sentences."

5. Iterative Refinement:

   • Experiment with different structures to identify the most effective prompt for your use case.

Common Pitfalls:

• Overloading the prompt with too much information.

• Using vague language or leaving the task open-ended.

• Ignoring user feedback or system-generated warnings.

Part 2: Using Retrieved Context to Enhance LLM Performance (5 Minutes)

Why Use Retrieved Context?

• LLMs have a fixed knowledge base limited by their training data. Providing retrieved context bridges the gap by feeding real-time or domain-specific information.

How to Incorporate Context:

1. Retrieve Relevant Data:

   • Use tools like FAISS or vector databases to fetch contextually relevant embeddings.

   • Example: Retrieve employee profiles for a query about "Python development."

2. Embed Context in the Prompt:

   • Present the retrieved data as part of the input.

   • Example:

     • Retrieved Context: "Alice is a Python Developer in AI Development. Bob is a Data Scientist with Python expertise."

     • Prompt: "Based on the profiles above, who would be best suited for a Python-related task?"

3. Enhance Responses:

   • Context ensures the response is grounded in factual and relevant information.

   • Example:

     • Without Context: "I don’t have enough information to answer."

     • With Context: "Alice and Bob are suitable for Python-related tasks based on their expertise."

Part 3: Examples of Prompts for Different Tasks (8 Minutes)

1. Summarization

• Task: Summarize text into concise bullet points.

Prompt:
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Summarize the following report into three key bullet points:

Report: [Insert text here]

• Example Response:

  • Bullet 1: Increased customer satisfaction by 20% in Q1.

  • Bullet 2: Reduced churn rate by 15%.

  • Bullet 3: Launched three new product features.

2. Question Answering

• Task: Provide an answer based on retrieved context.

Prompt:
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Context: Alice is a Python Developer in AI Development. Bob is a Data Scientist with Python expertise.

Question: Who is best suited to handle a Python-related task?

• Example Response:

  • "Alice and Bob both have Python expertise, but Alice specializes in AI applications."

3. Classification

• Task: Categorize text into predefined labels.

Prompt:
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Classify the following text into one of these categories: Technical Issue, Billing Query, or General Inquiry.

Text: [Insert text here]

• Example Response:

  • "The text falls under the category: Technical Issue."

4. Translation

• Task: Translate text while maintaining tone.

Prompt:
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Translate the following text from English to French while maintaining a professional tone:

Text: [Insert text here]

• Example Response:

  • "La traduction est effectuée de manière professionnelle."

Conclusion and Key Takeaways (5 Minutes)

Recap:

1. Effective Prompt Design: Clear, specific, and contextual prompts improve LLM performance.

2. Using Retrieved Context: Grounding responses in real-time or domain-specific data enhances relevance and accuracy.

3. Examples: Apply these techniques to tasks like summarization, question answering, classification, and translation.

Call to Action:

• Experiment with different prompts and refine them for your specific use cases.

• Explore tools like OpenAI and HuggingFace to build and test prompts.

• Start integrating retrieved context into your LLM workflows to unlock the full potential of your applications.

Objective

1. 1. Experiment with different prompt structures and designs for various tasks.

   2. Evaluate and compare the quality of responses generated by the LLM based on these prompt variations.

Step-by-Step Instructions

Step 1: Setup

Install Required Libraries

Ensure the necessary tools are installed:

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pip install openai pandas

Initialize the OpenAI API

Set up your API key to use OpenAI’s models:

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import openai

# Set your OpenAI API key

client = OpenAI(api_key="your_openai_api_key")

Step 2: Define Tasks and Prompts

We will experiment with three tasks: summarization, question answering, and classification.

Task 1: Summarization

Dataset:
Use a sample text to summarize:

python

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sample_text = """

Our company achieved a 20% increase in customer satisfaction in the last quarter by implementing new features in our app.

We also reduced churn rates by 15% and expanded into two new markets, increasing overall revenue by 25%.

"""

Prompt Variations:

Basic Prompt:
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basic_prompt = f"Summarize the following text:\n{sample_text}"

Structured Prompt:
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structured_prompt = f"Summarize the following text into three concise bullet points:\n{sample_text}"

Context-Driven Prompt:
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context_driven_prompt = f"Summarize this report into actionable insights for our team:\n{sample_text}"

Generate Responses:

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def generate_response(prompt):

response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[ {"role": "user", "content": prompt} ], max_tokens=100 )

return response['choices'][0]['text'].strip()

# Test each prompt

print("Basic Prompt Response:")

print(generate_response(basic_prompt))

print("\nStructured Prompt Response:")

print(generate_response(structured_prompt))

print("\nContext-Driven Prompt Response:")

print(generate_response(context_driven_prompt))

Task 2: Question Answering

Context and Query:

python

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context = "Alice is a Python Developer in AI Development. Bob is a Data Scientist with Python expertise."

query = "Who is best suited for a Python-related task?"

Prompt Variations:

Basic Prompt:
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basic_qa_prompt = f"{query}"

Contextual Prompt:
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contextual_qa_prompt = f"Context: {context}\n\n{query}"

Instructional Prompt:
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instructional_qa_prompt = f"Use the context below to determine the best person for a Python-related task:\nContext: {context}\n\n{query}"

Generate Responses:

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# Test each prompt

print("Basic QA Prompt Response:")

print(generate_response(basic_qa_prompt))

print("\nContextual QA Prompt Response:")

print(generate_response(contextual_qa_prompt))

print("\nInstructional QA Prompt Response:")

print(generate_response(instructional_qa_prompt))

Task 3: Classification

Text for Classification:

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classification_text = "The customer reported an error when uploading files to the application."

Prompt Variations:

Basic Prompt:
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basic_classification_prompt = f"Classify the following text:\n{classification_text}"

Label-Specific Prompt:
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label_prompt = f"Classify the following text as one of these categories: Technical Issue, Billing Query, or General Inquiry.\n\n{classification_text}"

Instructional Prompt:
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instructional_classification_prompt = f"Analyze the text below and determine which category it belongs to: Technical Issue, Billing Query, or General Inquiry.\n\nText: {classification_text}"

Generate Responses:

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# Test each prompt

print("Basic Classification Prompt Response:")

print(generate_response(basic_classification_prompt))

print("\nLabel-Specific Prompt Response:")

print(generate_response(label_prompt))

print("\nInstructional Classification Prompt Response:")

print(generate_response(instructional_classification_prompt))

Step 3: Evaluate Responses

Evaluation Criteria

1. Clarity: Is the response easy to understand and free of ambiguity?

2. Relevance: Does the response address the task effectively?

3. Accuracy: How well does the response align with the desired outcome?

4. Conciseness: Is the response appropriately concise and to the point?

Click here to see Sample Evaluation Table

Step 4: Iterate and Optimize

• Refine the prompts based on evaluation results.

• Test additional variations, such as adjusting tone or adding more context.

• Experiment with other tasks to generalize your findings.

Expected Outcomes

1. Insights into Prompt Design:

   • Understanding which designs work best for specific tasks.

2. Improved LLM Performance:

   • Higher-quality, task-specific responses.

3. Documentation of Results:

   • A structured evaluation to guide future prompt designs.

Objective

In this activity, you will:

1. Design prompt templates that effectively incorporate retrieved data.

2. Address three specific tasks:

   • Generating employee expertise summaries.

   • Contextual question answering.

   • Personalized recommendations.

Step-by-Step Instructions

Step 1: Prepare Retrieved Data

Ensure you have a structured dataset of retrieved employee profiles. Use the following example:

Retrieved Data Example:

python

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retrieved_profiles = [

    {"name": "Alice", "role": "Python Developer", "skills": ["Python", "Machine Learning"], "department": "AI Development"},

    {"name": "Bob", "role": "Data Scientist", "skills": ["Data Analysis", "Python", "Statistics"], "department": "Data Analytics"},

    {"name": "Carol", "role": "Backend Engineer", "skills": ["API Development", "Database Management"], "department": "API Integrations"}

]

Step 2: Prompt Templates

Task 1: Generating Employee Expertise Summaries

Prompt Objective: Summarize the expertise of retrieved employees in a concise and structured format.

Prompt Template:

text

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Using the information below, generate a concise summary of each employee's expertise:

Data:

- Name: {name_1}, Role: {role_1}, Skills: {skills_1}, Department: {department_1}

- Name: {name_2}, Role: {role_2}, Skills: {skills_2}, Department: {department_2}

- Name: {name_3}, Role: {role_3}, Skills: {skills_3}, Department: {department_3}

Summarize their expertise as a list of bullet points.

Python Implementation:

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def generate_expertise_prompt(profiles):

    data = "\n".join(

        f"- Name: {profile['name']}, Role: {profile['role']}, Skills: {', '.join(profile['skills'])}, Department: {profile['department']}"

        for profile in profiles

    )

    prompt = f"Using the information below, generate a concise summary of each employee's expertise:\n\nData:\n{data}\n\nSummarize their expertise as a list of bullet points."

    return prompt

Task 2: Contextual Question Answering

Prompt Objective: Answer a specific question based on retrieved employee data.

Prompt Template:

text

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Context:

- Alice is a Python Developer in AI Development, skilled in Python and Machine Learning.

- Bob is a Data Scientist in Data Analytics, skilled in Data Analysis, Python, and Statistics.

- Carol is a Backend Engineer in API Integrations, skilled in API Development and Database Management.

Question: {user_question}

Answer the question using the context above.

Python Implementation:

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def generate_qa_prompt(profiles, question):

    context = "\n".join(

        f"- {profile['name']} is a {profile['role']} in {profile['department']}, skilled in {', '.join(profile['skills'])}."

        for profile in profiles

    )

    prompt = f"Context:\n{context}\n\nQuestion: {question}\n\nAnswer the question using the context above."

    return prompt

Task 3: Personalized Recommendations

Prompt Objective: Provide personalized recommendations for a specific user request.

Prompt Template:

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Based on the retrieved employee profiles, recommend the most suitable person for the following request:

Request: {user_request}

Profiles:

- Name: {name_1}, Role: {role_1}, Skills: {skills_1}, Department: {department_1}

- Name: {name_2}, Role: {role_2}, Skills: {skills_2}, Department: {department_2}

- Name: {name_3}, Role: {role_3}, Skills: {skills_3}, Department: {department_3}

Recommendation:

Identify the top 1-2 most suitable employees for this request and explain why.

Python Implementation:

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def generate_recommendation_prompt(profiles, request):

    profiles_data = "\n".join(

        f"- Name: {profile['name']}, Role: {profile['role']}, Skills: {', '.join(profile['skills'])}, Department: {profile['department']}"

        for profile in profiles

    )

    prompt = f"Based on the retrieved employee profiles, recommend the most suitable person for the following request:\n\nRequest: {request}\n\nProfiles:\n{profiles_data}\n\nRecommendation:\nIdentify the top 1-2 most suitable employees for this request and explain why."

    return prompt

Step 3: Testing the Prompts

Sample Input and Output

Input for Expertise Summaries:

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print(generate_expertise_prompt(retrieved_profiles))

Output:

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Using the information below, generate a concise summary of each employee's expertise:

Data:

- Name: Alice, Role: Python Developer, Skills: Python, Machine Learning, Department: AI Development

- Name: Bob, Role: Data Scientist, Skills: Data Analysis, Python, Statistics, Department: Data Analytics

- Name: Carol, Role: Backend Engineer, Skills: API Development, Database Management, Department: API Integrations

Summarize their expertise as a list of bullet points.

Input for Contextual QA:

python

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print(generate_qa_prompt(retrieved_profiles, "Who can help with machine learning?"))

Output:

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Context:

- Alice is a Python Developer in AI Development, skilled in Python and Machine Learning.

- Bob is a Data Scientist in Data Analytics, skilled in Data Analysis, Python, and Statistics.

- Carol is a Backend Engineer in API Integrations, skilled in API Development and Database Management.

Question: Who can help with machine learning?

Answer the question using the context above.

Input for Recommendations:

python

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print(generate_recommendation_prompt(retrieved_profiles, "Help with building an API."))

Output:

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Based on the retrieved employee profiles, recommend the most suitable person for the following request:

Request: Help with building an API.

Profiles:

- Name: Alice, Role: Python Developer, Skills: Python, Machine Learning, Department: AI Development

- Name: Bob, Role: Data Scientist, Skills: Data Analysis, Python, Statistics, Department: Data Analytics

- Name: Carol, Role: Backend Engineer, Skills: API Development, Database Management, Department: API Integrations

Recommendation:

Identify the top 1-2 most suitable employees for this request and explain why.

Expected Outcomes

1. Reusable Prompt Templates:

   • Modular templates that can handle similar tasks across different datasets.

2. Enhanced LLM Performance:

   • Accurate, relevant, and contextually informed responses.

3. Scalability:

   • Adaptable templates for a variety of use cases and organizational needs.