The Secret Weapon: Prompt Engineering: 🪄

Introduction

Crafting the perfect prompt is like whispering secret instructions to your AI muse. But there’s no one-size-fits-all approach! Exploring different types of prompts unlocks a spectrum of possibilities in your Generative AI creations. Here’s a sneak peek:

How to start your AI Journey?

Different Types of Prompt Engineering

1. The Classic Prompt: ✍️

Straightforward and clear, laying out the desired task or output.
Think: “Write a poem about a robot falling in love with the moon.”
Ideal for beginners and specific, focused tasks.

2. The N-Shot Prompt:

Provide a few examples (N) of the desired output to guide the AI.
Imagine: “Show me 3 examples of poems about nature written in free verse, then write your own.”
Useful for refining style and direction, fostering consistency.

3. The Chain-of-Thought Prompt:

Break down complex tasks into smaller, sequential steps.
Picture: “1. Identify emotions in this image. 2. Generate captions expressing those emotions in different tones. 3. Merge captions into a cohesive paragraph.”
Perfect for tackling intricate tasks, and promoting logical flow.

4. The Conditioned Prompt:

Sets specific constraints or parameters to shape the output.
Think: “Write a children’s story filled with puns, but avoid using clichés.”
Adds a layer of challenge and control, encouraging creative exploration within boundaries.

5. The Open-Ended Prompt:

Offers minimal guidance, leaving the AI to fill in the blanks.
Imagine: “Create a world where animals speak and build cities. Tell me a story about its most daring explorer.”
Sparks innovation and unexpected results, ideal for brainstorming and discovery.

6. Interactive Prompts: ️

Leverage user input or external data to dynamically adjust the narrative.
Picture: “You are a detective in a cyberpunk city. Tell me what you see as I describe the crime scene.”
Creates an immersive, personalized experience, blurring the lines between creator and creation.

Points to remember while writing prompt

Use clear and concise language in prompts.
Provide relevant context and data.
Adjust temperature for creativity or accuracy.
Experiment with different prompt formats and lengths.
Integrate model responses into Salesforce workflows using provided APIs.

Examples of Different Prompts

Classic Insight Prompt

Prompt: Analyze historical sales data of tech clients and identify key trends in purchasing behaviour.

				
					// Query tech client sales data
List<Opportunity> techSales = [
    SELECT Id, CloseDate, Amount, Product.Name
    FROM Opportunity
    WHERE AccountId IN (SELECT Id FROM Account WHERE Industry = 'Technology')
    AND IsClosed = true
    ORDER BY CloseDate DESC
];

// Calculate quarterly sales trends
Map<Date, Decimal> quarterlySales = new Map<Date, Decimal>();
for (Opportunity opp : techSales) {
    Date quarterStart = Date.newInstance(opp.CloseDate.year(), opp.CloseDate.month() - opp.CloseDate.month() % 3, 1);
    quarterlySales.put(quarterStart, quarterlySales.get(quarterStart) + opp.Amount);
}

// Identify top products by revenue
Map<String, Decimal> productRevenue = new Map<String, Decimal>();
for (Opportunity opp : techSales) {
    productRevenue.put(opp.Product.Name, productRevenue.get(opp.Product.Name) + opp.Amount);
}

// Display insights for developers to interpret
System.debug('Quarterly Sales Trends: ' + quarterlySales);
System.debug('Top Products by Revenue: ' + productRevenue);

Personalized Customer Prompt

Prompt: Recommend three relevant products for Customer X based on recent website visits and past purchases.

				
					// Gather customer data
Contact customerX = [SELECT Id, Name, RecentVisitedPages, PastPurchases FROM Contact WHERE Name = 'Customer X'];

// Collect product recommendations from Einstein (replace with your actual Einstein model)
List<ProductRecommendation> recommendations = EinsteinRecommendations.getRecommendationsForContact(customerX);

// Display top 3 recommendations
System.debug('Recommended Products for Customer X:');
for (Integer i = 0; i < 3; i++) {
    System.debug('- ' + recommendations[i].Product.Name);
}

Data-Driven Content Prompt

Prompt: Create a blog post addressing top customer concerns from surveys and support tickets.

				
					// Gather customer feedback data
List<Case> supportTickets = [SELECT Id, Subject, Description FROM Case WHERE Status = 'Closed' AND Type = 'Problem'];
List<SurveyResponse> surveyResponses = [SELECT Id, Question, Answer FROM SurveyResponse];

// Extract common topics and concerns
Set<String> topConcerns = new Set<String>();
for (Case ticket : supportTickets) {
    topConcerns.addAll(extractKeywords(ticket.Subject, ticket.Description));
}
for (SurveyResponse response : surveyResponses) {
    topConcerns.addAll(extractKeywords(response.Question, response.Answer));
}

// Generate blog content
String blogPost = '**Top Customer Concerns and How We're Addressing Them**\n\n';
for (String concern : topConcerns) {
    blogPost += '* **' + concern + ':**\n';
    blogPost += '    - Explanation of the concern\n';
    blogPost += '    - Steps we're taking to address it\n';
}

// Display blog post for developers to review and publish
System.debug(blogPost);

Predictive Scenario Prompt

Prompt 1: Predict the likelihood of a lead converting to an opportunity based on historical data and current lead attributes.

				
					public class LeadConversionPredictor {

    public PredictionResult predictLeadConversion(Lead lead) {
        // 1. Gather historical data for lead conversion analysis
        List<Lead> historicalLeads = [SELECT Id, ConvertedDate, ... FROM Lead WHERE ...];
        // 2. Train a prediction model (e.g., logistic regression, decision tree)
        Model model = trainPredictionModel(historicalLeads);
        // 3. Extract relevant lead attributes
        Map<String, Object> leadFeatures = extractLeadFeatures(lead);
        // 4. Generate prediction using the model
        Probability prediction = model.predict(leadFeatures);
        // 5. Return prediction result
        return new PredictionResult(prediction);
    }

    // Helper methods for model training and feature extraction
    private Model trainPredictionModel(List<Lead> leads) {
        // Implement model training logic
    }

    private Map<String, Object> extractLeadFeatures(Lead lead) {
        // Map lead attributes to features for the model
    }
}

Prompt 2: Identify customers most likely to churn within the next 6 months, considering past purchase patterns, engagement metrics, and support interactions.

				
					public class ChurnPredictor {

    public List<Customer> predictHighChurnRiskCustomers() {
        // 1. Gather relevant customer data from multiple objects
        List<Customer> customers = [SELECT Id, ... FROM Customer];
        List<Order> orders = [SELECT Id, CustomerId, ... FROM Order];
        List<Case> cases = [SELECT Id, CustomerId, ... FROM Case];
        // 2. Calculate churn risk scores based on defined criteria
        Map<Id, Decimal> churnScores = calculateChurnScores(customers, orders, cases);
        // 3. Identify customers with scores above a threshold
        List<Customer> highChurnRiskCustomers = new List<Customer>();
        for (Customer customer : customers) {
            if (churnScores.get(customer.Id) > churnThreshold) {
                highChurnRiskCustomers.add(customer);
            }
        }
        return highChurnRiskCustomers;
    }

    // Helper method for churn score calculation
    private Map<Id, Decimal> calculateChurnScores(List<Customer> customers, List<Order> orders, List<Case> cases) {
        // Implement churn score logic
    }
}

N-Shot Prompt

Prompt:

Generate a list of 3 email subject lines that are likely to get high open rates, based on the following examples:

Example 1: Your free trial is about to expire!
Example 2: Don’t miss out on this exclusive offer!
Example 3: 5 secrets to boosting your sales productivity

Response

“Unlock VIP Access: Special Benefits Await You!”
“Flash Sale Alert: Limited-time Savings Inside!”
“Double Your Productivity with These Proven Strategies!”

Prompt: Write an Apex trigger that updates the ‘Last_Reviewed_Date’ field on the Account object to today’s date whenever a new Opportunity is created for that account.

				
					trigger UpdateContactLastContactedDate on Contact (after insert) {
    for (Contact c : Trigger.new) {
        c.Last_Contacted_Date__c = Date.today();
    }
}

Few Short Example

Prompt: Write an Apex method that calculates the total amount of all open opportunities for a given account, using only a few keywords as guidance: account ID, opportunity amount, and open opportunities.

Keywords:

account ID
opportunity amount
open opportunities

				
					public static Decimal calculateTotalOpenOpportunityAmount(Id accountId) {
    List<Opportunity> openOpps = [
        SELECT Amount
        FROM Opportunity
        WHERE AccountId = :accountId AND IsClosed = false
    ];

    Decimal totalAmount = 0;
    for (Opportunity opp : openOpps) {
        totalAmount += opp.Amount;
    }

    return totalAmount;
}

Resources

Exploring the world of prompt engineering can be exhilarating and empowering. To ignite your journey, here’s a curated list of resources and links to help you learn and master this fascinating skill:

Interactive Platforms:

Hugging Face’s Prompt Engineering Tutorial: Hands-on exploration with interactive challenges and code examples. https://huggingface.co/learn/nlp-course
TensorFlow Playground: Experiment with prompts and different AI models in a user-friendly environment. https://playground.tensorflow.org/
Colab Notebooks: Get creative with pre-built notebooks on various prompt engineering techniques. https://research.google.com/colaboratory/

Comprehensive Guides:

The Prompt Manual: Deep dive into the theory and practice of prompt engineering, with detailed examples. https://huggingface.co/docs/peft/conceptual_guides/prompting
AI Dungeon’s Prompt Engineering Guide: Practical tips and strategies for crafting effective prompts. https://help.aidungeon.com/
PromptCraft: A community-driven resource with curated prompts, tutorials, and challenges. https://medium.com/@mcraddock/the-art-of-prompt-crafting-3fe057b8b566

Blogs and Articles:

Two Minute Papers: Bite-sized explainer videos on exciting advancements in AI, including prompt engineering. https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg
Hugging Face Blog: Stay updated on the latest developments and research in the field. https://huggingface.co/blog
Lex Fridman Podcast: Profound conversations with leading AI researchers, often delving into prompt engineering. https://lexfridman.com/podcast/

Online Courses:

Udacity’s Intro to Generative AI: Structured learning path covering concepts like prompts and GANs. https://www.udacity.com/collection/generative-ai-courses/all/any-price/any-school/any-skill/any-difficulty/any-duration/any-type/most-popular/page-1
DeepMind’s Deep Learning Specialization: Dive into various AI techniques, including prompt engineering for text generation. https://www.coursera.org/specializations/deep-learning
Kaggle Learn: Free tutorials and courses on AI and machine learning, often touching upon prompt engineering. https://www.kaggle.com/learn

Community and Discussion:

Hugging Face Forum: Connect with other prompt engineering enthusiasts, share ideas, and learn from each other. https://discuss.huggingface.co/
AI Dungeon Discord: Join a vibrant community discussing AI creativity and experimenting with prompts. https://www.reddit.com/r/AIDungeon/comments/qepgu4/does_ai_dungeon_discord_server_still_exist/
Reddit’s r/PromptEngineering: Stay abreast of the latest trends and discussions on prompt engineering. https://www.reddit.com/r/PromptEngineering/
Prompt Engineering – https://www.pantherschools.com/how-to-start-your-ai-journey/