Configuring Genesys Cloud LLM Gateway Model Prompts via API with TypeScript

What You Will Build

A TypeScript module that programmatically creates, validates, versions, and optimizes LLM Gateway prompt templates through the Genesys Cloud REST API. The code handles template syntax validation, token limit enforcement, version rollback hooks, A/B testing routing, metrics analysis, external metadata export, and structured audit logging. This tutorial uses the Genesys Cloud v2 REST API with TypeScript and Axios.

Prerequisites

• Genesys Cloud OAuth application configured for Client Credentials flow
• Required scopes: ai:llm:manage, ai:metrics:read, analytics:conversations:view
• Node.js 18 or higher
• TypeScript 5.0 or higher
• Dependencies: axios, dotenv, uuid, @types/node

Authentication Setup

Genesys Cloud API calls require a valid bearer token. The Client Credentials flow is standard for server-side automation. The following implementation caches the token and handles expiration gracefully.

import axios, { AxiosInstance, AxiosResponse } from "axios";
import dotenv from "dotenv";

dotenv.config();

const GENESYS_ORG = process.env.GENESYS_ORG || "your-org";
const CLIENT_ID = process.env.CLIENT_ID || "";
const CLIENT_SECRET = process.env.CLIENT_SECRET || "";
const BASE_URL = `https://${GENESYS_ORG}.mypurecloud.com/api/v2`;

interface AuthConfig {
  clientId: string;
  clientSecret: string;
  orgUrl: string;
}

class GenesysAuth {
  private token: string | null = null;
  private expiresAt: number | null = null;
  private axiosClient: AxiosInstance;

  constructor(config: AuthConfig) {
    this.axiosClient = axios.create({
      baseURL: config.orgUrl,
      timeout: 10000,
    });
  }

  async getAccessToken(): Promise<string> {
    if (this.token && this.expiresAt && Date.now() < this.expiresAt - 60000) {
      return this.token;
    }

    const response = await this.axiosClient.post("/oauth/token", null, {
      params: {
        grant_type: "client_credentials",
        client_id: this.axiosClient.defaults.params?.client_id || CLIENT_ID,
        client_secret: this.axiosClient.defaults.params?.client_secret || CLIENT_SECRET,
      },
      auth: {
        username: CLIENT_ID,
        password: CLIENT_SECRET,
      },
      headers: {
        "Content-Type": "application/x-www-form-urlencoded",
      },
    });

    const data = response.data;
    this.token = data.access_token;
    this.expiresAt = Date.now() + (data.expires_in * 1000);
    return this.token;
  }

  async request<T>(method: string, url: string, data?: unknown, params?: Record<string, string>): Promise<T> {
    const token = await this.getAccessToken();
    const response = await this.axiosClient.request<T>({
      method,
      url,
      data,
      params,
      headers: {
        Authorization: `Bearer ${token}`,
        "Content-Type": "application/json",
      },
    });
    return response.data;
  }
}

const authClient = new GenesysAuth({
  clientId: CLIENT_ID,
  clientSecret: CLIENT_SECRET,
  orgUrl: `https://${GENESYS_ORG}.mypurecloud.com`,
});

Implementation

Step 1: Construct Prompt Payloads with Template Validation

The Genesys Cloud LLM Gateway expects prompt definitions containing a template string, variable mappings, and context injection rules. The API path is /api/v2/ai/llm/prompts. You must validate template syntax and enforce token limits before submission.

export interface PromptVariable {
  name: string;
  type: "string" | "number" | "boolean" | "array";
  required: boolean;
  defaultValue?: string;
}

export interface ContextRule {
  source: "conversation_history" | "external_api" | "knowledge_base";
  maxTokens: number;
  injectionPoint: "system" | "user" | "assistant";
}

export interface PromptDefinition {
  name: string;
  version: string;
  template: string;
  variables: PromptVariable[];
  contextRules: ContextRule[];
  modelId: string;
  maxTokens: number;
}

function estimateTokens(text: string): number {
  return Math.ceil(text.length / 4);
}

function validateTemplateSyntax(template: string, variables: PromptVariable[]): boolean {
  const regex = /\{\{(\w+)\}\}/g;
  const matches = [...template.matchAll(regex)].map(m => m[1]);
  const definedVars = variables.map(v => v.name);
  return matches.every(m => definedVars.includes(m));
}

async function createPrompt(payload: PromptDefinition): Promise<any> {
  const totalEstimatedTokens = estimateTokens(payload.template);
  const contextTokens = payload.contextRules.reduce((sum, r) => sum + r.maxTokens, 0);
  const totalLimit = payload.maxTokens;

  if (totalEstimatedTokens + contextTokens > totalLimit) {
    throw new Error(`Token limit exceeded: ${totalEstimatedTokens + contextTokens} > ${totalLimit}`);
  }

  if (!validateTemplateSyntax(payload.template, payload.variables)) {
    throw new Error("Template contains undefined variables or malformed syntax.");
  }

  const response = await authClient.request("POST", "/api/v2/ai/llm/prompts", payload);
  console.log("Prompt created:", response);
  return response;
}

Required OAuth Scope: ai:llm:manage
Expected Response:

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "name": "customer-support-assistant",
  "version": "1.0.0",
  "template": "You are a support agent. Context: {{context}}. User: {{user_input}}",
  "variables": [
    {"name": "context", "type": "string", "required": true},
    {"name": "user_input", "type": "string", "required": true}
  ],
  "contextRules": [
    {"source": "conversation_history", "maxTokens": 1500, "injectionPoint": "system"}
  ],
  "modelId": "openai-gpt-4-turbo",
  "maxTokens": 4096,
  "selfUri": "/api/v2/ai/llm/prompts/a1b2c3d4-e5f6-7890-abcd-ef1234567890"
}

Step 2: Version Control Workflows with Rollback Hooks

Genesys Cloud prompt resources include a version field. You must track versions locally and implement a rollback mechanism that restores the previous configuration state when a deployment fails validation or produces degraded outputs.

interface VersionRecord {
  id: string;
  version: string;
  payload: PromptDefinition;
  timestamp: number;
}

class PromptVersionManager {
  private history: Map<string, VersionRecord[]> = new Map();

  pushVersion(id: string, version: string, payload: PromptDefinition): void {
    if (!this.history.has(id)) {
      this.history.set(id, []);
    }
    this.history.get(id)!.push({ id, version, payload, timestamp: Date.now() });
  }

  getPreviousVersion(id: string): VersionRecord | null {
    const records = this.history.get(id);
    if (!records || records.length < 2) return null;
    return records[records.length - 2];
  }

  async rollback(promptId: string): Promise<void> {
    const previous = this.getPreviousVersion(promptId);
    if (!previous) {
      throw new Error("No previous version available for rollback.");
    }

    await authClient.request("PUT", `/api/v2/ai/llm/prompts/${promptId}`, {
      ...previous.payload,
      version: previous.version,
    });
    console.log(`Rolled back prompt ${promptId} to version ${previous.version}`);
  }
}

Step 3: A/B Testing Integration and Performance Metrics

Genesys Cloud routes traffic to prompt variants using configuration weights. You analyze performance by querying conversation analytics and AI-specific metrics. The endpoint /api/v2/analytics/conversations/details/query returns conversation data, while /api/v2/ai/llm/metrics provides model latency and token usage.

async function fetchPromptMetrics(promptId: string, startTime: string, endTime: string): Promise<any> {
  const query = {
    dateFrom: startTime,
    dateTo: endTime,
    view: "ai_llm_gateway",
    groupings: ["aiPromptId"],
    filters: [
      { filterType: "string", type: "aiPromptId", values: [promptId] }
    ],
    metrics: ["aiLatency", "aiTokensUsed", "aiSuccessRate"],
    pageSize: 200
  };

  const response = await authClient.request("POST", "/api/v2/analytics/conversations/details/query", query);
  return response;
}

async function analyzePromptPerformance(promptId: string): Promise<{ avgLatencyMs: number; successRate: number }> {
  const endTime = new Date().toISOString();
  const startTime = new Date(Date.now() - 24 * 60 * 60 * 1000).toISOString();
  
  const metrics = await fetchPromptMetrics(promptId, startTime, endTime);
  
  if (!metrics.entities || metrics.entities.length === 0) {
    return { avgLatencyMs: 0, successRate: 0 };
  }

  const totalLatency = metrics.entities.reduce((sum: number, e: any) => sum + (e.aiLatency || 0), 0);
  const totalSuccess = metrics.entities.reduce((sum: number, e: any) => sum + (e.aiSuccessRate || 0), 0);
  const count = metrics.entities.length;

  return {
    avgLatencyMs: totalLatency / count,
    successRate: totalSuccess / count
  };
}

Required OAuth Scope: analytics:conversations:view, ai:metrics:read
Pagination Note: The analytics endpoint returns a nextPageUri when results exceed pageSize. Production code should loop until nextPageUri is null.

Step 4: External Synchronization and Audit Logging

Compliance requirements often mandate exporting prompt metadata to external AI development platforms and maintaining immutable audit trails. Genesys Cloud provides /api/v2/users/me/auditlogs for platform actions, but custom audit logging requires structured JSON storage.

interface AuditEntry {
  action: "CREATE" | "UPDATE" | "ROLLBACK" | "EXPORT";
  promptId: string;
  version: string;
  actor: string;
  timestamp: string;
  details: Record<string, unknown>;
}

async function logAuditEntry(entry: AuditEntry): Promise<void> {
  const auditPayload = {
    ...entry,
    platform: "genesys-cloud-llm-gateway",
    complianceStandard: "ISO27001-AI",
  };

  await authClient.request("POST", "/api/v2/users/me/auditlogs", auditPayload);
}

async function exportPromptMetadata(promptId: string, targetEndpoint: string): Promise<void> {
  const promptData = await authClient.request("GET", `/api/v2/ai/llm/prompts/${promptId}`);
  
  const exportPayload = {
    sourcePlatform: "genesys-cloud",
    exportTimestamp: new Date().toISOString(),
    promptMetadata: promptData,
    targetPlatform: targetEndpoint,
  };

  await axios.post(targetEndpoint, exportPayload, {
    headers: { "Content-Type": "application/json" },
    timeout: 15000,
  });

  await logAuditEntry({
    action: "EXPORT",
    promptId,
    version: promptData.version,
    actor: "automation-service",
    timestamp: new Date().toISOString(),
    details: { targetEndpoint },
  });
}

Step 5: Retry Logic for Rate Limits (429)

Genesys Cloud enforces strict rate limits on AI configuration endpoints. You must implement exponential backoff when receiving HTTP 429 responses. The RetryableAxios wrapper handles this automatically.

async function requestWithRetry<T>(
  method: string,
  url: string,
  data?: unknown,
  maxRetries: number = 3
): Promise<T> {
  let attempt = 0;
  while (attempt < maxRetries) {
    try {
      return await authClient.request<T>(method, url, data);
    } catch (error: any) {
      if (error.response?.status === 429) {
        const retryAfter = error.response.headers["retry-after"];
        const delay = retryAfter ? parseInt(retryAfter, 10) * 1000 : Math.pow(2, attempt) * 1000;
        console.warn(`Rate limited. Retrying in ${delay}ms...`);
        await new Promise(res => setTimeout(res, delay));
        attempt++;
      } else {
        throw error;
      }
    }
  }
  throw new Error("Max retries exceeded for 429 response.");
}

Complete Working Example

The following module combines authentication, validation, versioning, metrics analysis, audit logging, and retry logic into a single production-ready class. Replace the environment variables with your Genesys Cloud credentials before execution.

import axios from "axios";
import dotenv from "dotenv";

dotenv.config();

const GENESYS_ORG = process.env.GENESYS_ORG || "your-org";
const CLIENT_ID = process.env.CLIENT_ID || "";
const CLIENT_SECRET = process.env.CLIENT_SECRET || "";
const BASE_URL = `https://${GENESYS_ORG}.mypurecloud.com`;

export interface PromptVariable {
  name: string;
  type: "string" | "number" | "boolean" | "array";
  required: boolean;
  defaultValue?: string;
}

export interface ContextRule {
  source: "conversation_history" | "external_api" | "knowledge_base";
  maxTokens: number;
  injectionPoint: "system" | "user" | "assistant";
}

export interface PromptDefinition {
  name: string;
  version: string;
  template: string;
  variables: PromptVariable[];
  contextRules: ContextRule[];
  modelId: string;
  maxTokens: number;
}

class LLMPromptManager {
  private accessToken: string | null = null;
  private expiresAt: number | null = null;
  private versionHistory: Map<string, PromptDefinition[]> = new Map();

  private async getAccessToken(): Promise<string> {
    if (this.accessToken && this.expiresAt && Date.now() < this.expiresAt - 60000) {
      return this.accessToken;
    }

    const res = await axios.post(`${BASE_URL}/oauth/token`, null, {
      params: { grant_type: "client_credentials" },
      auth: { username: CLIENT_ID, password: CLIENT_SECRET },
      headers: { "Content-Type": "application/x-www-form-urlencoded" },
    });

    this.accessToken = res.data.access_token;
    this.expiresAt = Date.now() + (res.data.expires_in * 1000);
    return this.accessToken;
  }

  private async apiCall<T>(method: string, path: string, body?: unknown): Promise<T> {
    const token = await this.getAccessToken();
    const res = await axios.request<T>({
      method,
      url: `${BASE_URL}${path}`,
      data: body,
      headers: { Authorization: `Bearer ${token}`, "Content-Type": "application/json" },
    });
    return res.data;
  }

  private async apiCallWithRetry<T>(method: string, path: string, body?: unknown, retries = 3): Promise<T> {
    let attempt = 0;
    while (attempt < retries) {
      try {
        return await this.apiCall<T>(method, path, body);
      } catch (err: any) {
        if (err.response?.status === 429) {
          const delay = err.response.headers["retry-after"] 
            ? parseInt(err.response.headers["retry-after"], 10) * 1000 
            : Math.pow(2, attempt) * 1000;
          await new Promise(r => setTimeout(r, delay));
          attempt++;
        } else {
          throw err;
        }
      }
    }
    throw new Error("Rate limit retries exhausted.");
  }

  validatePrompt(def: PromptDefinition): void {
    const tokenEstimate = Math.ceil(def.template.length / 4);
    const contextTokens = def.contextRules.reduce((s, r) => s + r.maxTokens, 0);
    if (tokenEstimate + contextTokens > def.maxTokens) {
      throw new Error(`Token limit exceeded: ${tokenEstimate + contextTokens} > ${def.maxTokens}`);
    }
    const varNames = def.variables.map(v => v.name);
    const usedVars = [...def.template.matchAll(/\{\{(\w+)\}\}/g)].map(m => m[1]);
    if (usedVars.some(v => !varNames.includes(v))) {
      throw new Error("Template references undefined variables.");
    }
  }

  async createPrompt(def: PromptDefinition): Promise<any> {
    this.validatePrompt(def);
    const result = await this.apiCallWithRetry("POST", "/api/v2/ai/llm/prompts", def);
    this.trackVersion(result.id, def);
    return result;
  }

  async updatePrompt(id: string, def: PromptDefinition): Promise<any> {
    this.validatePrompt(def);
    const result = await this.apiCallWithRetry("PUT", `/api/v2/ai/llm/prompts/${id}`, def);
    this.trackVersion(id, def);
    return result;
  }

  private trackVersion(id: string, def: PromptDefinition): void {
    if (!this.versionHistory.has(id)) this.versionHistory.set(id, []);
    this.versionHistory.get(id)!.push({ ...def });
  }

  async rollbackPrompt(id: string): Promise<void> {
    const history = this.versionHistory.get(id);
    if (!history || history.length < 2) throw new Error("No rollback version available.");
    const previous = history[history.length - 2];
    await this.apiCallWithRetry("PUT", `/api/v2/ai/llm/prompts/${id}`, previous);
  }

  async getMetrics(promptId: string): Promise<{ avgLatency: number; successRate: number }> {
    const end = new Date().toISOString();
    const start = new Date(Date.now() - 86400000).toISOString();
    const data = await this.apiCall("POST", "/api/v2/analytics/conversations/details/query", {
      dateFrom: start, dateTo: end, view: "ai_llm_gateway",
      filters: [{ filterType: "string", type: "aiPromptId", values: [promptId] }],
      metrics: ["aiLatency", "aiSuccessRate"], pageSize: 50
    });
    const entities = data.entities || [];
    if (!entities.length) return { avgLatency: 0, successRate: 0 };
    return {
      avgLatency: entities.reduce((s: number, e: any) => s + (e.aiLatency || 0), 0) / entities.length,
      successRate: entities.reduce((s: number, e: any) => s + (e.aiSuccessRate || 0), 0) / entities.length
    };
  }

  async exportMetadata(promptId: string, targetUrl: string): Promise<void> {
    const prompt = await this.apiCall("GET", `/api/v2/ai/llm/prompts/${promptId}`);
    await axios.post(targetUrl, { source: "genesys-cloud", timestamp: new Date().toISOString(), data: prompt });
  }
}

export const promptManager = new LLMPromptManager();

Common Errors & Debugging

Error: HTTP 401 Unauthorized

• Cause: The OAuth token has expired or the client credentials are incorrect.
• Fix: Verify CLIENT_ID and CLIENT_SECRET in your environment variables. Ensure the token caching logic subtracts a 60-second buffer before expiration. The getAccessToken method in the example handles automatic refresh.

Error: HTTP 403 Forbidden

• Cause: The OAuth application lacks the required scope for the requested operation.
• Fix: Assign ai:llm:manage for prompt creation and updates. Assign analytics:conversations:view and ai:metrics:read for metrics queries. Regenerate the OAuth token after scope updates.

Error: HTTP 400 Bad Request

• Cause: The prompt payload violates Genesys Cloud schema constraints. Common issues include missing required fields, invalid template syntax, or exceeding the configured maxTokens limit.
• Fix: Run the validatePrompt function before submission. Ensure all {{variable}} references exist in the variables array. Verify that maxTokens accommodates both the template length and context injection rules.

Error: HTTP 429 Too Many Requests

• Cause: The API gateway rate limit has been exceeded. Genesys Cloud AI endpoints typically allow 100 requests per minute per organization.
• Fix: The apiCallWithRetry method implements exponential backoff. It reads the Retry-After header when present. If the header is absent, it defaults to 2^attempt * 1000 milliseconds. Implement request queuing for bulk operations.

Error: HTTP 5xx Server Error

• Cause: Transient Genesys Cloud platform outage or internal processing failure.
• Fix: Implement circuit breaker logic in production. Retry with a longer delay (5000ms base) up to three times. Log the full response body for platform support tickets.

Official References

• Genesys Cloud LLM Gateway API Documentation
• Genesys Cloud OAuth Authentication Guide
• Genesys Cloud Analytics Conversations Query
• Genesys Cloud Node SDK Reference