Deploying the LLM Prompt Builder

This step is not required, but the LLM Prompt Builder is a no-code tool that lets prompt engineers test new prompts across LLMs, compare the results, version their experiments, and turn the best prompt into a model for further consumption in the platform (for example in SAS Intelligent Decisioning).

Importing the Prompt Builder

The Prompt Builder can also be imported via the SAS-Viya-Integrations/SAS-Agentic-AI-Accelerator-Prompt-Builder.json transfer package, that you can import using the SAS Environment Manager Import page, if you do you can skip ahead to step 3.

Standalone build

The Prompt Builder is shipped as a standalone application in the LLM-Prompt-Builder directory of this repository. It has no longer has any dependency on the SAS Portal Framework for SAS Viya — it is a single self-contained HTML file that you embed directly in a SAS Visual Analytics report through SAS Job Execution.

1. Get the single-file app

You have two options:

Option A — use the prebuilt file (recommended). Download the ready-to-use dist/index.html from the repository. No Node.js or build step is required — skip straight to step 2. Or copy the code from there to your clipboard and move to step 2.

Option B — build it yourself. With Node.js (^18 || ^20 || >=22) installed:

git clone https://github.com/sassoftware/sas-agentic-ai-accelerator.git
cd sas-agentic-ai-accelerator/LLM-Prompt-Builder
npm install
npm run build

This produces the same single-file dist/index.html. See the project README for local development and customization details.

2. Create a SAS Job Execution definition

The single-file app is served to Visual Analytics through SAS Job Execution:

In SAS Data and AI Studio, click on New > Job definition to create a new Job Definition
In the Job Definition (edit) tab go to the right hand side bar and click on the first icon (Associate a Form) and change it from Prompt designer to HTML form.
Copy the full contents of dist/index.html and paste it into the Job Definition (edit) tab
Save the job to the place you want it in SAS Content - note that people that should be using it need read access.
On the right hand side bar click the second icon (Properties), expand the Details section and copy the value under Job URL - it should look something like this: https://your-viya-host/SASJobExecution/SASJobExecution/?_program=path-to-your-job-definition after the path value add the following: &_action=form, as this enables us to view the HTML form without having to provide any SAS code. Save that URL as we will need it in fifth step.

Why the build base64-encodes its scripts

SAS Job Execution serves HTML through a Go template engine that treats {{ … }} as directives. The build base64-encodes every inline <script> and decodes it at runtime so the minified bundle — which inevitably contains {{/}} — is not corrupted. This is automatic and requires no action.

3. Create the API Key data source

The API keys used for the Prompt Builder are stored in a CAS table, this enables you to create one report with applied row-level-security (please add your own columns, the script below only provides the bare bones) and also enables easy key rotation by updating the table. Replace the key values with your API keys and also if you do not want store it in your CASUSER change the CAS Library.

cas mySess;

data work.LLM_API_KEYS;
    length KeyName $ 64 KeyValue $ 512;
    label KeyName  = "Key Name"
          KeyValue = "API Key";
    infile datalines dlm='|' truncover;
    input KeyName $ KeyValue $;
    datalines;
Anthropic|REPLACE_WITH_YOUR_ANTHROPIC_API_KEY
OpenAI|REPLACE_WITH_YOUR_OPENAI_API_KEY
Google|REPLACE_WITH_YOUR_GOOGLE_API_KEY
;
run;

proc casUtil inCASLib='casuser' outCASLib='casuser';
    dropTable casData='LLM_API_KEYS' quiet;
    load data=work.LLM_API_KEYS casOut='LLM_API_KEYS';
    promote casData='LLM_API_KEYS' casOut='LLM_API_KEYS';
    save casData='LLM_API_KEYS' casOut='LLM_API_KEYS' replace;
quit;

cas mySess terminate;

API keys (assigned data)

The API keys are the entries under API_KEYS in that same llm-prompt-builder.json. They are supplied through the object's assigned data — never the URL or Properties panel — so they never appear in the report definition or a shareable link. Assign a data source with two columns, one provider per row:

Column	Meaning
1st	Key name — the `API_KEYS` entry name, which must match the `API_KEY.default` value referenced by an LLM's `options.json` (e.g. `Anthropic`, `OpenAI`, `Google`).
2nd	Key value — the actual API key.

Keep keys governed

Keeping API keys in a governed CAS/data source (rather than the URL) means they are never persisted in the report definition or a shareable link. Restricting read access to that data source restricts who can run paid model calls.

4. SAS Environment Manager configuration

To let the app run inside SAS Visual Analytics, set the following Content Security Policy directives via SAS Environment Manager → Configuration → View Definitions.

SAS Visual Analytics — sas.commons.web.security → SAS Visual Analytics → content-security-policy:

default-src 'self'; script-src 'self' 'wasm-unsafe-eval'; style-src 'self' 'unsafe-inline'; img-src * blob: data:; frame-src * blob: data: mailto:; connect-src 'self' *.sas.com login.microsoftonline.com graph.microsoft.com *.arcgis.com *.arcgisonline.com; object-src 'none'

SAS Job Execution — sas.commons.web.security → SAS Job Execution → content-security-policy (replace <sas-viya-host> with your environment's URL). The 'unsafe-inline' and 'unsafe-eval' in script-src are what allow the base64-decoded single-file bundle to run:

default-src 'self'; script-src 'self' 'unsafe-inline' 'unsafe-eval' https://<sas-viya-host>; style-src 'self' 'unsafe-inline'; img-src * blob: data:; child-src 'self' blob: data: ; frame-ancestors 'self'; form-action 'self';

IFrame sandbox — verify the sas.visualanalytics definition's IFrame Sandbox Attribute Value contains at minimum allow-same-origin allow-scripts.

note

That sandbox does not include allow-popups, which is why the "Open in SAS Model Manager" link copies the URL instead of forcing a new tab on a normal click (use the browser's right-click → Open link in new tab). Add allow-popups to the IFrame Sandbox Attribute Value to let that link open a tab directly.

Restart the following kubernetes pods of your SAS Viya environment if saving the changes didn't trigger a restart automatically:

sas-job-execution
sas-job-execution-app
sas-logon-app
sas-visual-analytics
sas-visual-analytics-app

Example kubectl command:

kubectl get pods -n <your-namespace> -o name | grep -E 'pod/sas-job-execution|pod/sas-logon-app|pod/sas-visual-analytics' | xargs kubectl delete -n <your-namespace>

5. Add the object to a Visual Analytics report

If you imported the SAS-Agentic-AI-Accelerator-Prompt-Builder.json package then open up the SAS Visual Analytics report under SAS Content > SAS Agentic AI Accelerator > Prompt Builder > Prompt Builder and then continue.

In a Visual Analytics report, add a Data-Driven Content object and in the Options pane under Web Content enter the URL from the previous step or if you imported it update the base URL to your SAS Viya server.
Assign the API-key data source (see below) to the object's data role. If you moved the CAS table from CASUSER to another place you will have to replace the data source.
Open the object's Properties panel and set the configuration values (see below).

Configuration (Properties panel)

The environment-specific values below are exactly those captured in your llm-prompt-builder.json — the file produced in the Setup SAS Model Manager chapter (regenerate it by re-running mdb setup if you have lost it). Copy each value from that file into the object's Properties panel:

Properties-panel field	`llm-prompt-builder.json` key	Meaning
SAS Viya host	(not in the file)	SAS Viya base URL. Leave blank to default to the embedding origin.
Model Manager repository ID	`modelRepositoryID`	Model Manager repository in which new prompt projects are created.
LLM project ID	`llmProjectID`	Model Manager project holding the available LLM definitions (each with an `options.json`).
SCR endpoint	`SCREndpoint`	Base URL of the SCR endpoint hosting the LLM containers.
Deployment type	`deploymentType`	`k8s` (default) or `aca` (Azure Container Apps / Instances). See Container Deployment.

info

Until these values are supplied the object shows a "Configuration required" message and does not call SAS Viya, so it never fails against placeholder IDs. The same parameters can also be appended to the object's URL by hand, which is a reliable fallback if your Visual Analytics version does not render the options panel.

1. Get the single-file app​

2. Create a SAS Job Execution definition​

3. Create the API Key data source​

API keys (assigned data)​

4. SAS Environment Manager configuration​

5. Add the object to a Visual Analytics report​

Configuration (Properties panel)​