Thursday 18 April 2024

Chaining Einstein Copilot Custom Actions

Image created by DALL-E 3 based in a prompt by Bob Buzzard


I've been testing out Salesforce's Einstein Copilot assistant for a few weeks now, but typically I've been tacking a single custom action onto the end of one or more standard actions, and usually a Prompt Template where I can easily ground it with some data from a specific record. 

Something that hasn't been overly clear to me is how I can link a couple of custom actions and pass information other than record ids between them. The kind of information that would be shown to the user if it was a single action, or the kind of information that the user had to provide in a request.


The other aspect I was interested in was some kind of DML. I've seen examples where a record gets created or fields get changed based on a request, but the beta standard actions don't have this capability, so it was clear I'd need to build that myself. So the scenario I came up with was : Given an opportunity, it's existing related activities, and a few rules (like if it's about to close someone should be in contact every day), Copilot suggests a follow up task and inserts it into the Salesforce database. 

This can't easily (or sensibly, really) be done in a single custom action. I guess I could create a custom Apex action that uses a Prompt Template to get a task recommendation from the LLM and then inserts it, but it seems a bit clunky and isn't overly reusable. What I want here are two custom actions:

  1. A Prompt Template custom action that gets the suggestion for the task from the LLM
  2. An Apex custom action that takes the details of the suggestion and uses it to create a Salesforce task.
I also don't want to have to expend a lot of effort processing the recommendation response to pick out the important details - if I'm writing that much code I might as well figure out the task that I need as well.


The key aspect of the Prompt Template is explaining how I want the response to be formatted, so that it can be easily processed by Apex code. I was half expecting to have to use few shot prompting to get this to work, but was pleasantly surprised to find that I could just describe it in natural language with a few rules about labelling:
Generate a recommendation for a follow up task in JSON format, including the following details:
- The subject of the task with the element label 'subject'
- A brief description with the element label 'description' - this should include the names of anyone other than the user responsible for the task who should be involved
- The date the task should be completed by, in DD/MM/YYYY format with the element label' due_date'
- The record id of the user who is responsible for the task with the element label 'user_id'
- {!$Input:Candidate_Opportunity.Id} with the element label 'what_id'

Do not include any supporting text, output JSON only.
Note that I did have to remind it not to add any text over and above the JSON output - this is something LLMs tend to suffer from a lot in my experience, always wanting to a add a "here you go" or "as requested". Nice that they try to be polite, but not helpful when you need structured data!

Trying this out in Copilot Builder showed that I was getting the output that I wanted:

Note that while there is the additional text of 'Great, I have generated ...', that's Copilot rather than the LLM, so if I can chain this to another custom action I'll just get the JSON format data.

My Apex Custom Action code is surprisingly svelte:

public with sharing class CopilotOppFollowUpTask 
    @InvocableMethod(label='Create Task' description='Creates a task')
    public static List<String> createTask(List<String> tasksJSON) {

        JSONParser parser=JSON.createParser(tasksJSON[0]);
        Map<String, String> params=new Map<String, String>();

        while (null!=parser.nextToken()) 
            if (JSONToken.FIELD_NAME==parser.getCurrentToken())
                String name=parser.getText();
                String value=parser.getText();
                System.debug('Name = ' + name + ', Value = ' + value);
                params.put(name, value);

        String dateStr=params.get('due_date');

        Date dueDate=date.newInstance(Integer.valueOf(dateStr.substring(6,10)),
                                      Integer.valueOf(dateStr.substring(3, 5)),
                                      Integer.valueOf(dateStr.substring(0, 2)));

        Task task=new Task(Subject=params.get('subject'),

        insert task;
        return new List<String>{'Task created with id ' + task.Id};

All that's needed to make it available for a Custom Action is the invocable aspect :

    @InvocableMethod(label='Create Task' description='Creates a task')

When I define the custom action, it's all about the instruction:

which hopefully is enough for the Copilot reasoning engine to figure out it can use the JSON format output from the task recommendation action. Of course I still need to give Copilot the correct instruction so it understands it needs to chain the actions:

And here's the task it came up with:

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Sunday 7 April 2024

Einstein Copliot Custom Actions

Image created by DALL-E 3 based on a prompt by Bob Buzzard


One of the key features of Einstein Copilot from Salesforce is its extensibility - while there are a bunch of standard actions, and many more coming, there will always be something more you want to give your users. Custom actions allow you to create an AI assistant that targets your specific business challenges, adding capabilities to your copilot that generate real value for your users.


In my last post, I introduced the Sales Coach - a custom Lightning Web Component that you add to an Opportunity record page. When the Sales Coach component renders, it executes Apex code to hydrate a prompt template with details of the Opportunity and request some guidance from an LLM. 

While this works well for users that are viewing an Opportunity record, I also want to provide a way tfor users receive guidance on deals while they are in another part of the Salesforce application - their Home Page, for example.

Creating the Custom Copilot Action

Custom Copilot Actions can be created using Apex, Flow or Prompt Templates. I've gone for a Prompt Template, as I already have this in place for the Sales Coach component. Once I choose the Reference Action Type of Prompt Template, I get to select from a list of existing prompts in the Reference Action selector. 

Once I've chosen my action, I provide details of how the Copilot can use it :

The Copilot Action Instructions is pre-populated from my Prompt Template description. As I want to use it in exactly the same way, I can leave that alone. I then provide the instructions around the Candidate Opportunity input, and check the box in the Output section to Show in conversation - without this, the user won't be able to receive the coaching they so richly deserve.

Note that while the Prompt Template pull several fields from the Opportunity record - Name, Amount, Close Date, Stage Name - I don't need to specify any of this information here. I don't even need to specify that it's an Opportunity record - that is picked up automatically from the Prompt Template input. Pretty simple really.

Once my custom action is created, I need to add it to the Copilot. I do this via the Copilot Builder, selecting my new action from the list of available actions and then clicking a button to add it to the Active Copilot :

As an aside, this Copilot isn't Active yet, so the text in the button is slightly misleading, but not a big deal as I can only have one Copilot so I'm hardly likely to get confused.

Putting it Together

Remember earlier when I created the Copilot action I gave it details of how to use it? The Copilot Action Instructions field does what it says on the tin - it instructs the Copilot as to the purpose of the action and when it should be used:

I've defined this as providing advice to progress a specific opportunity, so in order to include the action in the Copilot plan, I need to ask the it to help me do that. As we're in AI world, I don't need to be as obvious as asking for advice to progress an opportunity, instead I used my own words and asked for "help to win a deal".

Interestingly, when I ran this to capture the screenshots, Copilot told me that it was following the instructions that I'd provided for the action, which I hadn't seen before:

Something to remember when using Prompt Template actions is they will lead to a slower response time, as each one involves a round trip to an LLM. There will always be one round trip, to create the plan, but if you manage to generate a request that involves multiple Prompt Template actions, there will be separate requests made for each of those, and you'll be consuming additional tokens. 

Monday 1 April 2024

Deploy Code at the Speed of AI with Ship Happens

Image from DALL-E 3 based on a prompt by Bob Buzzard


It's been quite the year for Salesforce Einstein - Copilot, Prompt Builder, lots of new Generative AI features, and it's only the beginning of April!

Einstein for Developers has been out in beta for a while now, built on CodeGen - Salesforce's in-house open-source LLM. To date this as been focused on generating code from natural language prompts, auto-completions. and unit tests, but now it's time for artificial intelligence to dip a toe into the Dev Ops space with Ship Happens.

Ship Happens

In keeping with most Generative AI launches, Einstein Dev Ops goes live with a single feature and the expectation that more will come in a few months. It's quite the feature though - with Ship Happens, your code will barely have a chance to touch down in a scratch org before it's flung to production and into the hands of users.

You've Got to be Shipping Me

Research shows that by far the biggest blocker to getting code into production is developers accepting that they have finished. While this sounds like a simple decision, developers can't help polishing - always looking to squeeze another feature in, or a percentage point of unit test coverage.  Ship Happens unblocks your development team by handing the decision off to Generative AI.

As development progresses, the code is constantly analysed by a Large Language Model trained on years of Salesforce deployments, both successful and unsuccessful, and the quality of code for your specific instance. Once the LLM decides the code is as good as it's ever going to be, it's committed to version control and on it's way to production before the developer knows what has hit them. To ensure full traceability, Ship Happens reuses the same commit message when it takes the decision to ship a feature - "You've Got to be Shipping Me".

Welcome to Ship Creek

Crunching the numbers after the pilot program showed that users are delighted when Ship Happens. An updated experience every time they login, reduced waiting time for new features, and the oh so familiar bugs they have come to expect with every release. Once they accept their role under our new AI overlords, your developers will enjoy the freedom to focus purely on writing code - with Ship Happens in charge, everyone knows that features will be released exactly when they should be, not a moment earlier and not a moment later. Before long, everyone will be happily floating along Ship Creek and forget they were ever in a different place.

Sunday 24 March 2024

Einstein Prompt Templates in Apex - the Sales Coach

Image by DALL-E 3 based on a prompt from Bob Buzzard


Thus far in my journey with Generative AI in Salesforce I've been focused on using prompt templates out of the box - sales emails, field generation, or in custom copilot actions. Salesforce isn't the boss of me though, so I'm also keen to figure out how to include prompts into my own applications.

The Use Case

I want a way for sales reps to get advice on what they should do with an opportunity - is it worth proceeding with, what does it make sense to do next, that kind of thing. I don't want this happening every time I load the page, as this will consume tokens and cost money, so I'm going to make it available as an LWC inside a tab that isn't the default on the opportunity page:

The Coach tab contains my LWC, but the user has to select the tab to receive the coaching. I could make this a button, but pressing buttons fulfils an innate desire for control and exploration in humans, so it could still end up consuming a lot of tokens. Opening tabs, for some reason, doesn't trigger the same urges.

The Implementation

The first thing to figure out was how to "execute" a prompt from Apex - how to hydrate the prompt with data about an opportunity, send it to a Large Language Model, and receive a response. This is achieved via the Apex ConnectAPI namespace. It's a bit of a multi-step process, so you should be prepared to write a little more code than you might expect.

Prepare the Input

The prompt template for coaching takes a single parameter - the id of the Opportunity the user is accessing. Prompt templates receive their inputs via a ConnectApi.​EinsteinPrompt​Template​GenerationsInput instance, which contains a map of ConnectApi.WrappedValue elements keyed by the API name of the parameter from the prompt template. The ConnectApi.WrappedValue is also a map though, in this case with a single entry of the opportunity id with the key value 'id'. As mentioned, a little more code than you might have expected to get a single id parameter passed to a prompt, but nothing too horrific:

ConnectApi.EinsteinPromptTemplateGenerationsInput promptGenerationsInput = 
                           new ConnectApi.EinsteinPromptTemplateGenerationsInput();

Map<String,ConnectApi.WrappedValue> valueMap = new Map<String,ConnectApi.WrappedValue>();

Map<String, String> opportunityRecordIdMap = new Map<String, String>();
opportunityRecordIdMap.put('id', oppId); 

ConnectApi.WrappedValue opportunityWrappedValue = new ConnectApi.WrappedValue();
opportunityWrappedValue.value = opportunityRecordIdMap;

valueMap.put('Input:Candidate_Opportunity', opportunityWrappedValue);

promptGenerationsInput.inputParams = valueMap;
promptGenerationsInput.isPreview = false;
Note that the ConnectApi.EinsteinPromptTemplateGenerationsInput handles more than just the inputs - for example, I've set the isPreview property to false, indicating that I don't just want to preview the prompt hydrated with data, I want to send it to the LLM and receive a response. There's also a ConnectApi.​EinsteinLlm​Additional​ConfigInput property to send granular configuration information such as temperature to the LLM

Make the Request

Once I have the input ready, I can send the request to the LLM.
ConnectApi.EinsteinPromptTemplateGenerationsRepresentation generationsOutput = 
Note that I've hardcoded the ID of the prompt template in the request. I really didn't want to do this, but I can't find a way to query the prompt templates in the system. The metadata type is GenAiPromptTemplate, but this doesn't appear to be queryable, not by me at least! This is the name that getSObjectType returns, but it doesn't appear in the Salesforce Object Reference and both Apex and the Tooling API error when I try to use it in a query. I'm sure this is on its way, but if I put this into production I'll probably expose a property on the LWC so that whoever is building the record page can supply the Id there. And if it's my Evil Co-Worker, they can supply a completely different prompt template Id to introduce confusion and chaos, so everybody should be happy.

Extracting the Response

The output from the LLM is in the form of a ConnectAPI.EinsteinLLMGenerationItemOutput. This contains a generations property that is a list of responses from the LLM. Right now I'm just picking the first element in the list and pulling the text property containing the actual information that I want to display to the user.  I can also access the safety score if I want.

Putting it in front of the User

The Lightning Web Component is pretty vanilla - when the user opens the tab the record id is passed via a property setting decorated with @api. This triggers the request to the server to send the prompt to the LLM:
@api get recordId() { 
    return this._recordId; 

set recordId(value) {
    GetAdvice({oppId : this._recordId})
    .then(data => {

Note that I have to do this via an imperative Apex call rather than using the wire service, as calling an Apex ConnectAPI method consumes a DML call, which means my controller method can't be cacheable, which means it can't be wired.

The user receives a holding message that the coach is thinking:

Then after a few seconds, the Coach is ready to point them in the right direction:

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Saturday 16 March 2024

Einstein Prompt Grounding with Apex

Image generated by DALL-E 3 in response to a prompt from Bob Buzzard


A couple of weeks ago I published my initial experience and thoughts on Einstein Prompt Builder, and one of my findings was that I couldn't get grounding with Apex to work, even using the example code in the help. Thanks to some help from Claudio Moraes from the Salesforce Partner team, that is no longer the case and I can pull information into my prompt via an Apex class. 

Example Scenario

The prompt I've created is following up on a lead where we have a pretty good idea what product they are interested in. Using standard prompting I can get the user to select the product in questions, but I also want to include the standard price to give the lead an idea of what it might cost them. I can pull the PricebookEntries related list from the product into the prompt, but that is all or nothing, whereas I just want the a single entry from the standard price book.

The Apex Code

In order to be considered for use in a Sales Email Prompt Template, I need a method with the following signature:

@InvocableMethod(label='Price for Lead' 
                 description='Gets the price of a product for a lead' 
public static List<Response> getProductStandardPrices(List<Request> requests) 

The key aspects of this are:

It must be an invocable method

Which simply means it needs the @InvocableMethod annotation. Without this, the Apex class will never be considered by Prompt Builder.

It has to have the CapabilityType attribute on the @InvocableMethod annotation

This identifies the type of Prompt Template that the method can be used with. As this is for a Sales Email, I specify :


It must take a list of Request class instances

Request is an inner class that I have to define, with the format:

 public class Request {
    public User Sender;

    public Lead Recipient;

    public Product2 RelatedObject;

The variable names must be exactly as shown here - including the capitalisation (thanks Claudio!). These match the properties of the Prompt Template.

It must return a list of Response class instances

Again, Response is an inner class that I define, with the format:

 public class Response {
    public String prompt;

The body of the method itself is pretty straightforward:

@InvocableMethod(label='Price for Lead' 
                 description='Gets the price of a product for a lead' 
public static List<Response> getProductStandardPrices(List<Request> requests) 
    Request req=requests[0];
    Id productId=req.RelatedObject.Id;
    Response resp=new Response();
    PricebookEntry entry=[
            select Id, UnitPrice, CurrencyISOCode from PricebookEntry
            where Product2.Id=:productId
            and   Pricebook2.IsStandard = true];

    resp.prompt=entry.CurrencyISOCode + ' ' + entry.UnitPrice;
    return new List<Response>{resp};

Note that while the method has to take a list of requests and return a list of responses, there's only ever one element present.

Putting it Together

If I deploy my class and it doesn't show up in Prompt Builder because I've got the capitalisation wrong, it doesn't appear to be possible to recover it. Instead, I have to delete the class and try the next attempt with a new one (thanks again, Claudio!).  

Once I have the correct breed of chicken and the right colour chalk for the pentangle, Apex appears in the resource picker :

And clicking into the Apex option shows me the name of my enclosing class and the label attribute from the @InvocableMethod annotation :

And selecting this adds it to my prompt, helpfully highlighted to show me that it is a reference rather than plaintext :

The Prompt and Response

I've added this to a pretty simple prompt - I doubt it would make much difference to a potential customer, but the point of this blog is to show the Apex, not what makes a great prompt! Note that as I didn't ground the prompt with any details of the user, they are expected to add that themselves :

and the response from the LLM:


Thank You for Your Interest in Solar Panels


Dear Lauren Reynolds, 

Thank you for your interest in our solar panels. We are pleased to share the price with you. The cost for a single panel is USD 1725.00. Please note that discounts are available for larger orders, but this price already reflects an excellent value for such a high-quality product. 

We hope this information is helpful to you, and we look forward to the possibility of working with you. 

Should you have any further questions or require additional details, please do not hesitate to reach out. 

Warm regards,
[Your Name]
Credera Sales Team

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Sunday 10 March 2024

Hands on with Salesforce Copilot

Image generated by DALL-E 3 based on a prompt from Bob Buzzard


Copilot from Salesforce entered beta at the end of Feb '24, without too much of a build-up I think it's fair to say. Given how quiet the likes of Marc Benioff were on the socials, I was half-expecting it to be delayed until nearer to the Summer release. I'm very pleased that wasn't the case, but not as pleased as I was to get access to it.


Much like Prompt Builder, enabling copilot is pretty simple :

  • Go to Setup -> Einstein Generative AI -> Einstein Setup and toggle it on
  • The go to Setup -> Einstein Generative AI -> Einstein Copilot Studio (Beta) -> Einstein Copilots and toggle that on.
Copilot is then enabled in your org. It's not active though - you have to head into Builder and click the Activate button to see it in the user interface. You can play around with it pretty well inside the Builder though, so don't activate until you are happy.


Once you've activated your copilot, you'll see a new Enstein icon up at the top right of the user interface - looks a bit Heisenberg-esque to me, which I think is awesome:

Clicking this opens copilot in a new sidebar, moving the current screen contents over to the left rather than overlaying them - it's the little things:

One key point to note - copilot doesn't always have the context you are working in - the screengrab above is copilot opening while I'm viewing an opportunity. It's tempting to think that I can just refer to 'this opportunity' and it will pick up the details from my current context/URL, but that doesn't happen - instead it says 'Sure, which opportunity is this?"

As an aside, it also really wants me to use the name of the record - if I try the Id, it tells me that nothing was found in the query. This might seem limited, but I think it'sactually working exactly as intended. There's an action in this copilot configuration that retrieves a record and it requires the record name. If I want to use Ids instead, I need a different action.

Back to context though, if I click the Summarize Opportunity button, it does know about the opportunity and gives me the details : 

Making a Request

As always with generative AI, the quality of the response has a pretty direct correlation with the quality of the prompt. Just vaguely asking for help doesn't go well:

I then tried a request that I thought had enough detail, but in hindsight lacked a little clarity:

Third time's the charm though, and now I have a few accounts to focus on:


The standard functionality is all well and good, but I'm always interested in how I can extend and customise. There was a bit of trial and error involved, as the docs aren't perfect yet, but remember this is a beta feature.

I was trying to think of something that wasn't in place already, and after a short while it struck me - there's just not enough poetry in enterprise software nowadays, so an action to write a haiku about an account was the obvious, if not the only, choice.

This will be a Prompt Template action, so the first job is to define the prompt:

You are a world renowned haiku writer, who has a side gig as a CRM administrator.
Write a haiku based for the account {!$Input:Account.Name} which operates in the {!$Input:Account.Industry} industry. 
The haiku should not include any PII.

Then the copilot action: 

The important parts of the action are :

  • Copilot Action Instructions - this is used by the AI to determine whether an action is appropriate for a plan. The more detailed the better, although in this case there probably won't be multiple actions competing for the job of writing the haiku.
  • Require user confirmation - tick this if any data is going to be changed. 
  • Account Instructions - these are the instructions for the input named Account that I've specified in the prompt. I've identified which fields I'm interested in, the type, and that it's required. I'm not asking the user to enter any data and there's no PII to mask as I'm just using the Name and Industry fields, which is public information
  • Prompt Response Instructions - here I've defined what type of response, but most importantly ticked Show in conversation. Without this nothing will be returned to the user.
Once this action is added to my copilot, I can add writing a haiku to a request and get the user experience I've been craving:

More Information

Saturday 2 March 2024

Hands on with Prompt Builder

Image generated by DALL-E 3 based on a prompt created by Bob Buzzard


Unless you've been living under a rock, you'll have seen some big news from Salesforce in the Generative AI space this week (29th Feb 2024, for future readers) - Prompt Builder is Generally Available and Copilot is in Beta. 

I've been lucky enough to get my hands on both of these new features and have an initial dig around, so continue reading to find out how I got on with Prompt Builder. Like it's General Availability, my hands on experience with Copilot is coming soon!


Setting up Prompt Builder is very straightforward:

  1. Navigate to Setup -> Einstein Generative AI -> Einstein Setup and toggle it on
  2. Assign the Prompt Builder Manager/User permission sets
  3. Reload Setup, then navigate to Setup -> Einstein Generative AI -> Prompt Builder and start prompting.

Creating Prompts

I've mostly played with the Sales Emails prompts, aside from my Copilot experiments (see below). My initial use case was follow up emails for attendees of one of my World Famous Release Webinars, so I created a new custom object of Webinar and added a few fields that I wanted to pull into my prompt - the usual name and description, but also a couple of fields to capture the URLs of additional resources I wanted to send. I also added a Call to Action - this is a field to capture what I was hoping the attendees would do after attending - try out a couple of the features, for example.

Once I'd added the Webinar custom object, it didn't appear immediately in the prompt builder as a selectable Related Object. After checking the docs to make sure I hadn't missed a checkbox to make it available, I decided it must just be a timing thing - maybe the objects needed reindexing or the like. Then
I found that I couldn't create any prompts for a period of time - not sure how long, but it felt like 30-40 minutes - during that time the Related Object selector wouldn't populate at all. Then clearly whatever had been happening in the background finished, and I could select my new Webinar object.

My prompt is loosely based on the standard follow up email, but includes specific instructions and fields related to my webinar :

You are a Sales Executive and your name is {!$Input:Sender.Name} from an organization called {!$Input:Sender.CompanyName}. Your prospect is {!$Input:Recipient.Name}, who is the {!$Input:Recipient.Title}, from the company called {!$Input:Recipient.Company}.Your prospect {!$Input:Recipient.Name} attended a Salesforce release webinar that your company hosted.
When I ask you to generate a follow-up email to your prospect you must strictly follow my instructions below.
Instructions: """
The salutation must only contain the recipient's first name.
You must strictly not use "I hope this email finds you well" ,"I hope this email finds you doing well", or any other variation that expresses interest in the recipient's well-being to open the email.
Create a follow-up email conveying that you are following up on their attendance of {!$Input:Webinar__c.Name} to see if they need any additional information or support.
Mention the webinar, the number of attendees from {!$Input:Webinar__c.Attendee_Count__c} and include a summary of the {!$Input:Webinar__c.Description__c}. Express the hope that it was useful for them and that they will follow the call to action identified at {!$Input:Webinar__c.Call_to_Action__c}
End the email with details of the two additional resources that are available : {!$Input:Webinar__c.Resource_1__c} , which is {!$Input:Webinar__c.Resource_1_Detail__c} and {!$Input:Webinar__c.Resource_2__c}, which is {!$Input:Webinar__c.Resource_2_Detail__c}
Finish by indirectly encouraging your prospect {!$Input:Recipient.Name} to respond to your email by asking them if they need any further information, have questions or require assistance.
Generate a subject line that can increase open rate using words and content related to the email body.
Now generate the follow-up email to your prospect.

Grounding the Prompt

I then provide a couple of sample records to test the prompt out. The Lead record is one that came with the org and will probably be familiar to anyone that has used a Salesforce Developer Edition before - Lauren Bailey from Omega, Inc. The webinar record is as follows:

The resolution section is useful when you are grounding the prompt with a number of inputs - it shows the expanded merge fields so you can see exactly what you'll be sending the AI model (although sensitive data will be masked by the Einstein Trust Layer, obviously):
You are a Sales Executive and your name is Keir Bowden from an organization called Credera. Your prospect is Lauren Bailey, who is the Director of Services, from the company called Omega,Inc.Your prospect Lauren Bailey attended a Salesforce release webinar that your company hosted.

When I ask you to generate a follow-up email to your prospect you must strictly follow my instructions below.

Instructions: """
The salutation must only contain the recipient's first name.
You must strictly not use "I hope this email finds you well" ,"I hope this email finds you doing well", or any other variation that expresses interest in the recipient's well-being to open the email.

Create a follow-up email conveying that you are following up on their attendance of Spring 24 Release Webinar to see if they need any additional information or support.

Mention the webinar, the number of attendees from 150 and include a summary of the Learn about the key new features and enhancements in the Spring 24 release of Salesforce.. Express the hope that it was useful for them and that they will follow the call to action identified at Enable dynamic forms and add related objects to your records home pages.

End the email with details of the two additional resources that are available : , which is Salesforce Release Notes and, which is Webinar Recording

Finish by indirectly encouraging your prospect Lauren Bailey to respond to your email by asking them if they need any further information, have questions or require assistance.

Generate a subject line that can increase open rate using words and content related to the email body.

Now generate the follow-up email to your prospect.
I found that my original calls to action, even though they looked good in the record, didn't work very well when included in the prompt, so always have a look at the resolution the first couple of times.

The Response

The response from OpenAI GPT 3.5 Turbo was pretty good I thought, although maybe a little brusque - I'll probably rework it to a slightly more chatty version, but it's a great start for a small amount of work:
Enhance Your Salesforce Experience with Spring 24 Release Insights

Hello Lauren,

I trust you found the Spring 24 Release Webinar insightful. With 150 attendees, we hope you gained valuable insights into the key new features and enhancements in the Salesforce Spring 24 release. The webinar highlighted how to enable dynamic forms and add related objects to your records home pages.

For further exploration, two additional resources are available:
- Salesforce Release Notes:
- Webinar Recording:

Should you require any additional information, have questions, or need assistance, please feel free to reach out.

Best regards,
Keir Bowden

Grounding with Apex/Flow

I can see I'll be doing a lot of grounding with pro or low code - if I'm sending an email regarding a product I'll probably want to include a rate card and maybe some contractual information for example.  Grounding with Flow is very straightforward, and once you choose a Template Triggered Prompt Flow and pick the Sales Emails capability, there's a nice screen to define the object types you are using :

Apex is a little different - you define a class with an invocable method that defines the capability, then define Request and Response inner classes to manage the input and output parameters:

@InvocableMethod(label='Price for Lead' description='Gets the price of a product for a lead' 
public static List<Response> getProductStandardPrices(List<Request> requests)  
        --- --- ---
public class Request {
    public User sender;

    public Lead recipient;

    public Webinar__c relatedObject;

public class Response {
    public String Prompt;

Apex is also a little different in that I couldn't get it to work! Even after copy/pasting the example in the Salesforce help, it wouldn't show up in the Resources selector. I'm sure this will be resolved soon, and as Prompt Builder has been one of Salesforce's more rapid features to go GA, I'm okay with their being a few glitches. As I've written before, I'll take a bit of additional effort tracking down issues if it means I get my hands on things earlier.

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