@memberjunction/ai-prompts

Advanced AI prompt execution engine for MemberJunction. Provides hierarchical template composition, intelligent model selection with failover, parallel execution with judge-based result selection, structured output validation with retry, comprehensive execution tracking, and streaming support. This is the primary interface for executing AI prompts in the MemberJunction framework.

Guides

• Assistant Prefill & Stop Sequences — How to use assistantPrefill and stopSequences to control output format, reduce token usage, and eliminate verbose format instructions from prompts.

Architecture

graph TD
    subgraph "@memberjunction/ai-prompts"
        PR["AIPromptRunner"]
        style PR fill:#2d8659,stroke:#1a5c3a,color:#fff

        EP["ExecutionPlanner"]
        style EP fill:#7c5295,stroke:#563a6b,color:#fff

        PEC["ParallelExecutionCoordinator"]
        style PEC fill:#7c5295,stroke:#563a6b,color:#fff

        PE["ParallelExecution"]
        style PE fill:#7c5295,stroke:#563a6b,color:#fff
    end

    subgraph "Execution Pipeline"
        T["1. Template Rendering
Handlebars + System Placeholders"]
        style T fill:#b8762f,stroke:#8a5722,color:#fff

        MS["2. Model Selection
Default / Specific / ByPower"]
        style MS fill:#b8762f,stroke:#8a5722,color:#fff

        EX["3. LLM Execution
With Streaming & Caching"]
        style EX fill:#b8762f,stroke:#8a5722,color:#fff

        VAL["4. Output Validation
JSON Schema + Retry"]
        style VAL fill:#b8762f,stroke:#8a5722,color:#fff

        TRK["5. Execution Tracking
AIPromptRun Records"]
        style TRK fill:#b8762f,stroke:#8a5722,color:#fff
    end

    PR --> EP
    PR --> PEC
    PEC --> PE
    PR --> T
    T --> MS
    MS --> EX
    EX --> VAL
    VAL --> TRK

    subgraph Dependencies
        AI["@memberjunction/ai
BaseLLM"]
        style AI fill:#2d6a9f,stroke:#1a4971,color:#fff

        ACP["@memberjunction/ai-core-plus
AIPromptParams"]
        style ACP fill:#2d6a9f,stroke:#1a4971,color:#fff

        AIE["@memberjunction/aiengine
AIEngine"]
        style AIE fill:#2d6a9f,stroke:#1a4971,color:#fff

        TMPL["@memberjunction/templates
TemplateEngine"]
        style TMPL fill:#2d6a9f,stroke:#1a4971,color:#fff

        CRED["@memberjunction/credentials
CredentialEngine"]
        style CRED fill:#2d6a9f,stroke:#1a4971,color:#fff
    end

    AI --> PR
    ACP --> PR
    AIE --> PR
    TMPL --> PR
    CRED --> PR
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Installation

npm install @memberjunction/ai-prompts
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Key Features

Hierarchical Template Composition

Build complex prompts from reusable sub-templates with unlimited nesting depth:

import { AIPromptRunner } from '@memberjunction/ai-prompts';
import { AIPromptParams, ChildPromptParam } from '@memberjunction/ai-core-plus';

const runner = new AIPromptRunner();

// Parent template uses {{ analysis }} and {{ summary }} placeholders
const params = new AIPromptParams();
params.prompt = parentPrompt;
params.childPrompts = [
    new ChildPromptParam(analysisParams, 'analysis'),
    new ChildPromptParam(summaryParams, 'summary')
];
params.data = { userInput: 'complex data to process' };

const result = await runner.ExecutePrompt(params);
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Execution order:

1. Child prompts render depth-first (children before parents)
2. Sibling prompts at each level execute in parallel
3. Child results replace placeholders in parent template
4. Final composed prompt executes as a single LLM call

Model Selection Strategies

Three strategies for selecting which AI model executes a prompt:

Model selection precedence (highest to lowest):

1. AIPromptParams.override -- Runtime model/vendor override
2. AIPromptParams.modelSelectionPrompt -- Alternate prompt for model config
3. Prompt's own model configuration (strategy + associations)

Credential-evaluation short-circuit (performance): Candidates are ordered by priority, so once the runner finds the highest-priority candidate that has working credentials it stops probing the remaining candidates and records them as "not-evaluated" in the ModelSelection telemetry. This avoids running a credential/env-var check for every configured model on every prompt run. The full ordered candidate list is still used for failover, so this only trims per-candidate availability telemetry for the tail. To force a complete availability report for every candidate (e.g. an admin diagnostic), set AIPromptParams.forceFullModelEvaluation = true.

Parallel Execution with Judging

Execute prompts across multiple models simultaneously and select the best result:

• Configurable execution groups with different models
• AI judge prompt evaluates and ranks results
• Automatic selection of best result based on judge scoring
• Full tracking of all parallel results

Output Validation and Retry

Automatic validation of AI outputs with configurable retry:

• JSON schema validation against OutputExample definitions
• Automatic JSON repair via JSON5 parsing and LLM-based repair
• Configurable retry count with the original or repaired prompts
• Validation syntax cleaning (removes ?, *, :type markers from JSON keys)
• Detailed validation attempt tracking

Output type coercion (AIPrompt.OutputType): the raw model text is coerced before validation — string (verbatim), number (parseFloat, errors on NaN), boolean (true/yes/1 ↔ false/no/0, case-insensitive + trimmed), date (new Date, errors on invalid), and object (JSON). For object, the text is first run through CleanJSON (strips markdown fences etc.); if that fails and attemptJSONRepair is set, it retries via JSON5 and then LLM-based repair. When an OutputExample is defined, validation-syntax markers are stripped from result keys automatically. With skipValidation, coercion failures return the raw output instead of throwing.

Validation behavior (AIPrompt.ValidationBehavior): Strict retries up to MaxRetries on validation failure; Warn logs and returns the (invalid) output; None accepts as-is. The parsed OutputExample is cached by content so it isn't re-parsed on every run/retry.

Streaming Support

Real-time streaming of LLM responses:

const params = new AIPromptParams();
params.prompt = myPrompt;
params.onStreaming = (chunk) => {
    process.stdout.write(chunk.content);
};

const result = await runner.ExecutePrompt(params);
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Execution Tracking

Every prompt execution creates an AIPromptRun record with:

• Model and vendor used
• Template rendering results
• Token usage (prompt + completion)
• Cost tracking
• Execution time
• Parent/child relationships for hierarchical prompts
• Agent run linkage via agentRunId

Persistence is fire-and-forget. The initial Running INSERT and the final Completed/Failed UPDATE are queued, not awaited, so the model call is never blocked on a DB round-trip. Saves for the same run are chained (the INSERT always completes before the UPDATE, so a slow INSERT can't clobber the finalized row), and the record's ID is available immediately because NewRecord() client-generates the UUID. Save failures are logged but never fail the prompt (the record is observability, not part of the success contract). Callers that need the rows durably written before continuing can await runner.WaitForPendingPromptRunSaves().

Credential Resolution

Hierarchical credential resolution for API keys:

1. AIPromptParams.credentialId (per-request override)
2. AIPromptModel.CredentialID (prompt-model specific)
3. AIModelVendor.CredentialID (model-vendor specific)
4. AIVendor.CredentialID (vendor default)
5. AIPromptParams.apiKeys[] (legacy runtime keys)
6. AI_VENDOR_API_KEY__<DRIVER> environment variables (legacy)

Failover

When a model fails due to rate limiting, authentication errors, or other transient issues, the runner can automatically retry with alternate models from the selection candidates.

Usage

Basic Prompt Execution

import { AIPromptRunner } from '@memberjunction/ai-prompts';
import { AIPromptParams } from '@memberjunction/ai-core-plus';
import { AIEngine } from '@memberjunction/aiengine';

// Get prompt from metadata
await AIEngine.Instance.Config(false, contextUser);
const prompt = AIEngine.Instance.Prompts.find(p => p.Name === 'Summarize Content');

const runner = new AIPromptRunner();
const params = new AIPromptParams();
params.prompt = prompt;
params.data = { content: documentText, maxLength: 500 };
params.contextUser = contextUser;

const result = await runner.ExecutePrompt(params);

if (result.success) {
    console.log(result.result);             // Parsed/validated result
    console.log(result.promptTokens);       // Input tokens used
    console.log(result.completionTokens);   // Output tokens generated
    console.log(result.executionTimeMS);    // Execution duration
}
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With Progress Tracking

params.onProgress = (progress) => {
    console.log(`[${progress.step}] ${progress.percentage}% - ${progress.message}`);
};
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With Effort Level

params.effortLevel = 85; // High effort for thorough analysis (1-100 scale)
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With Runtime Model Override

params.override = {
    modelId: 'specific-model-id',
    vendorId: 'specific-vendor-id'
};
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Dependencies

• @memberjunction/ai -- Core AI abstractions (BaseLLM, ChatParams)
• @memberjunction/ai-core-plus -- AIPromptParams, AIPromptRunResult, extended entities
• @memberjunction/ai-engine-base -- AIEngineBase metadata cache
• @memberjunction/aiengine -- AIEngine server-side operations
• @memberjunction/core -- MJ framework core
• @memberjunction/core-entities -- Generated entity classes
• @memberjunction/credentials -- Credential resolution
• @memberjunction/templates -- Template rendering engine
• @memberjunction/templates-base-types -- Template base types
• json5 -- Lenient JSON parsing for repair