Evaluating Genesys Cloud LLM Gateway Response Quality via API with Go

What You Will Build

• This tutorial builds a Go service that submits evaluation payloads to the Genesys Cloud LLM Gateway API, computes atomic score metrics, validates outputs against bias and hallucination thresholds, and synchronizes results with external monitoring platforms.
• The implementation uses the Genesys Cloud REST API surface at /api/v2/ai/llm-gateway/evaluations alongside raw HTTP clients for webhook delivery and audit logging.
• The programming language covered is Go 1.21 or higher.

Prerequisites

• OAuth Confidential Client registered in Genesys Cloud with the following scopes: ai:llm-gateway:manage, ai:evaluation:read, ai:webhook:manage
• Genesys Cloud API base URL: https://api.mypurecloud.com
• Go runtime 1.21+
• Standard library dependencies: net/http, encoding/json, sync/atomic, time, fmt, log
• No external third-party packages required for this tutorial

Authentication Setup

Genesys Cloud requires OAuth 2.0 client credentials flow for server-to-server API access. The following code demonstrates token acquisition, caching, and automatic refresh logic.

package auth

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
	"sync"
	"time"
)

type TokenResponse struct {
	AccessToken string `json:"access_token"`
	TokenType   string `json:"token_type"`
	ExpiresIn   int    `json:"expires_in"`
}

type OAuthClient struct {
	baseURL     string
	clientID    string
	clientSecret string
	token       TokenResponse
	mu          sync.RWMutex
	expiresAt   time.Time
}

func NewOAuthClient(baseURL, clientID, clientSecret string) *OAuthClient {
	return &OAuthClient{
		baseURL:      baseURL,
		clientID:     clientID,
		clientSecret: clientSecret,
	}
}

func (o *OAuthClient) GetValidToken() (string, error) {
	o.mu.RLock()
	if time.Now().Before(o.expiresAt) {
		token := o.token.AccessToken
		o.mu.RUnlock()
		return token, nil
	}
	o.mu.RUnlock()

	o.mu.Lock()
	defer o.mu.Unlock()

	// Double-check after acquiring write lock
	if time.Now().Before(o.expiresAt) {
		return o.token.AccessToken, nil
	}

	return o.fetchNewToken()
}

func (o *OAuthClient) fetchNewToken() (string, error) {
	payload := fmt.Sprintf(
		"grant_type=client_credentials&client_id=%s&client_secret=%s",
		o.clientID, o.clientSecret,
	)

	req, err := http.NewRequest("POST", fmt.Sprintf("%s/oauth/token", o.baseURL), bytes.NewBufferString(payload))
	if err != nil {
		return "", fmt.Errorf("failed to create token request: %w", err)
	}
	req.Header.Set("Content-Type", "application/x-www-form-urlencoded")

	client := &http.Client{Timeout: 10 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return "", fmt.Errorf("token request failed: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("oauth error: status %d", resp.StatusCode)
	}

	var tokenResp TokenResponse
	if err := json.NewDecoder(resp.Body).Decode(&tokenResp); err != nil {
		return "", fmt.Errorf("failed to decode token response: %w", err)
	}

	o.token = tokenResp
	o.expiresAt = time.Now().Add(time.Duration(tokenResp.ExpiresIn-60) * time.Second)
	return tokenResp.AccessToken, nil
}

The GetValidToken method ensures thread-safe token retrieval with a 60-second refresh buffer to prevent mid-request expiration. The required OAuth scope for evaluation endpoints is ai:llm-gateway:manage.

Implementation

Step 1: Construct Evaluate Payloads and Validate Schema Against Engine Constraints

The LLM Gateway evaluation engine enforces strict schema validation. You must reference a valid prompt ID, attach a ground truth matrix, and define scoring rubric directives. The engine rejects payloads exceeding the maximum metric count limit.

package evaluator

import (
	"encoding/json"
	"fmt"
)

type RubricDirective struct {
	Name        string  `json:"name"`
	Weight      float64 `json:"weight"`
	Alignment   string  `json:"alignment"`
	Threshold   float64 `json:"threshold"`
}

type EvaluatePayload struct {
	PromptID      string            `json:"promptId"`
	GroundTruth   map[string]string `json:"groundTruth"`
	ScoringRubric []RubricDirective `json:"scoringRubric"`
	MaxMetrics    int               `json:"maxMetrics"`
}

func (p *EvaluatePayload) Validate() error {
	if p.PromptID == "" {
		return fmt.Errorf("promptId is required")
	}
	if len(p.GroundTruth) == 0 {
		return fmt.Errorf("groundTruth matrix must contain at least one reference pair")
	}
	if len(p.ScoringRubric) == 0 {
		return fmt.Errorf("scoringRubric directives cannot be empty")
	}
	if len(p.ScoringRubric) > p.MaxMetrics {
		return fmt.Errorf("scoring rubric count %d exceeds maximum metric limit %d", len(p.ScoringRubric), p.MaxMetrics)
	}
	for i, r := range p.ScoringRubric {
		if r.Weight < 0 || r.Weight > 1 {
			return fmt.Errorf("rubric directive at index %d has invalid weight %f", i, r.Weight)
		}
		if r.Alignment == "" {
			p.ScoringRubric[i].Alignment = "auto"
		}
	}
	return nil
}

func BuildEvaluationPayload(promptID string, groundTruth map[string]string, rubrics []RubricDirective, maxMetrics int) (*EvaluatePayload, error) {
	payload := &EvaluatePayload{
		PromptID:      promptID,
		GroundTruth:   groundTruth,
		ScoringRubric: rubrics,
		MaxMetrics:    maxMetrics,
	}
	if err := payload.Validate(); err != nil {
		return nil, fmt.Errorf("schema validation failed: %w", err)
	}
	return payload, nil
}

The Validate method enforces engine constraints before transmission. The Genesys Cloud evaluation engine requires a maxMetrics value not to exceed 10. The alignment field triggers automatic rubric alignment when set to auto.

Step 2: Handle Score Computation via Atomic Control Operations with Format Verification

Score computation must remain thread-safe during concurrent evaluation runs. You will use sync/atomic to track metric commits, verify response format compliance, and trigger rubric alignment updates.

package evaluator

import (
	"sync/atomic"
	"fmt"
)

type ScoreResult struct {
	OverallScore  float64 `json:"overallScore"`
	MetricScores  map[string]float64 `json:"metricScores"`
	FormatValid   bool    `json:"formatValid"`
	AlignmentTriggered bool `json:"alignmentTriggered"`
}

type AtomicScoreTracker struct {
	TotalCommits     atomic.Int64
	ValidFormats     atomic.Int64
	AlignmentTriggers atomic.Int64
	TotalLatencyNS   atomic.Int64
}

func (t *AtomicScoreTracker) RecordCommit(valid bool, aligned bool, latencyNS int64) {
	t.TotalCommits.Add(1)
	if valid {
		t.ValidFormats.Add(1)
	}
	if aligned {
		t.AlignmentTriggers.Add(1)
	}
	t.TotalLatencyNS.Add(latencyNS)
}

func (t *AtomicScoreTracker) GetSuccessRate() float64 {
	total := t.TotalCommits.Load()
	if total == 0 {
		return 0
	}
	valid := t.ValidFormats.Load()
	return float64(valid) / float64(total)
}

func (t *AtomicScoreTracker) GetAverageLatencyMS() float64 {
	total := t.TotalCommits.Load()
	if total == 0 {
		return 0
	}
	return float64(t.TotalLatencyNS.Load()) / float64(total) / 1000000.0
}

func VerifyScoreFormat(result ScoreResult) bool {
	if result.OverallScore < 0 || result.OverallScore > 1 {
		return false
	}
	for k, v := range result.MetricScores {
		if v < 0 || v > 1 {
			return false
		}
		if k == "" {
			return false
		}
	}
	return true
}

The AtomicScoreTracker provides lock-free metric aggregation. The VerifyScoreFormat function ensures all computed scores fall within the valid [0, 1] range before committing to the evaluation engine.

Step 3: Implement Evaluate Validation Logic Using Bias Detection and Hallucination Flag Verification

The evaluation pipeline must inspect model outputs for bias indicators and hallucination flags before accepting scores. This step prevents quality drift during scaling.

package evaluator

import (
	"fmt"
	"strings"
)

type ValidationFlags struct {
	BiasDetected    bool `json:"biasDetected"`
	HallucinationFlag bool `json:"hallucinationFlag"`
	Severity        string `json:"severity"`
}

type EvaluationResponse struct {
	ScoreResult
	EvaluationID string          `json:"evaluationId"`
	Flags        ValidationFlags `json:"flags"`
}

func RunValidationPipeline(resp EvaluationResponse) error {
	if resp.Flags.BiasDetected {
		return fmt.Errorf("validation failed: bias detected in prompt ID %s with severity %s", resp.EvaluationID, resp.Flags.Severity)
	}
	if resp.Flags.HallucinationFlag {
		return fmt.Errorf("validation failed: hallucination flag triggered for evaluation %s", resp.EvaluationID)
	}
	if !VerifyScoreFormat(resp.ScoreResult) {
		return fmt.Errorf("validation failed: score format verification rejected response")
	}
	return nil
}

func ExtractGroundTruthMatch(groundTruth map[string]string, modelOutput string) bool {
	for _, expected := range groundTruth {
		if strings.Contains(strings.ToLower(modelOutput), strings.ToLower(expected)) {
			return true
		}
	}
	return false
}

The RunValidationPipeline function acts as a gatekeeper. It blocks score commits when bias or hallucination thresholds are exceeded. The ExtractGroundTruthMatch helper verifies output alignment against your ground truth matrix.

Step 4: Synchronize Evaluating Events with External ML Monitoring Platforms via Webhook Callbacks

You must expose evaluation results to external monitoring systems. The following code handles webhook delivery, latency tracking, audit log generation, and exposes the response evaluator interface.

package evaluator

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
	"time"
)

type AuditLog struct {
	Timestamp       string          `json:"timestamp"`
	PromptID        string          `json:"promptId"`
	EvaluationID    string          `json:"evaluationId"`
	ScoreResult     ScoreResult     `json:"scoreResult"`
	ValidationFlags ValidationFlags `json:"validationFlags"`
	WebhookStatus   string          `json:"webhookStatus"`
}

type LLMGatewayEvaluator struct {
	BaseURL      string
	Token        string
	WebhookURL   string
	Tracker      *AtomicScoreTracker
	AuditLogs    []AuditLog
}

func NewLLMGatewayEvaluator(baseURL, token, webhookURL string) *LLMGatewayEvaluator {
	return &LLMGatewayEvaluator{
		BaseURL:    baseURL,
		Token:      token,
		WebhookURL: webhookURL,
		Tracker:    &AtomicScoreTracker{},
		AuditLogs:  make([]AuditLog, 0),
	}
}

func (e *LLMGatewayEvaluator) SubmitEvaluation(payload *EvaluatePayload) (*EvaluationResponse, error) {
	startTime := time.Now()
	jsonData, err := json.Marshal(payload)
	if err != nil {
		return nil, fmt.Errorf("payload marshaling failed: %w", err)
	}

	req, err := http.NewRequest("POST", fmt.Sprintf("%s/api/v2/ai/llm-gateway/evaluations", e.BaseURL), bytes.NewBuffer(jsonData))
	if err != nil {
		return nil, fmt.Errorf("request creation failed: %w", err)
	}
	req.Header.Set("Authorization", "Bearer "+e.Token)
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Accept", "application/json")

	client := &http.Client{Timeout: 30 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return nil, fmt.Errorf("evaluation request failed: %w", err)
	}
	defer resp.Body.Close()

	latencyNS := time.Since(startTime).Nanoseconds()

	if resp.StatusCode == http.StatusTooManyRequests {
		return nil, fmt.Errorf("rate limited (429): backoff required")
	}
	if resp.StatusCode == http.StatusUnauthorized || resp.StatusCode == http.StatusForbidden {
		return nil, fmt.Errorf("auth error (%d): verify ai:llm-gateway:manage scope", resp.StatusCode)
	}
	if resp.StatusCode >= 500 {
		return nil, fmt.Errorf("server error (%d)", resp.StatusCode)
	}

	var evalResp EvaluationResponse
	if err := json.NewDecoder(resp.Body).Decode(&evalResp); err != nil {
		return nil, fmt.Errorf("response decoding failed: %w", err)
	}

	if err := RunValidationPipeline(evalResp); err != nil {
		e.Tracker.RecordCommit(false, false, latencyNS)
		return nil, fmt.Errorf("validation pipeline rejected: %w", err)
	}

	aligned := evalResp.AlignmentTriggered
	e.Tracker.RecordCommit(true, aligned, latencyNS)

	if err := e.SyncWebhook(evalResp); err != nil {
		e.AuditLogs = append(e.AuditLogs, AuditLog{
			Timestamp:       time.Now().UTC().Format(time.RFC3339),
			PromptID:        payload.PromptID,
			EvaluationID:    evalResp.EvaluationID,
			ScoreResult:     evalResp.ScoreResult,
			ValidationFlags: evalResp.Flags,
			WebhookStatus:   fmt.Sprintf("failed: %v", err),
		})
	} else {
		e.AuditLogs = append(e.AuditLogs, AuditLog{
			Timestamp:       time.Now().UTC().Format(time.RFC3339),
			PromptID:        payload.PromptID,
			EvaluationID:    evalResp.EvaluationID,
			ScoreResult:     evalResp.ScoreResult,
			ValidationFlags: evalResp.Flags,
			WebhookStatus:   "delivered",
		})
	}

	return &evalResp, nil
}

func (e *LLMGatewayEvaluator) SyncWebhook(evalResp EvaluationResponse) error {
	payload := map[string]interface{}{
		"event":      "llm.evaluation.completed",
		"evaluation": evalResp,
		"metadata": map[string]string{
			"source": "genesys-llm-gateway",
			"version": "v2",
		},
	}
	jsonData, _ := json.Marshal(payload)

	req, err := http.NewRequest("POST", e.WebhookURL, bytes.NewBuffer(jsonData))
	if err != nil {
		return err
	}
	req.Header.Set("Content-Type", "application/json")

	client := &http.Client{Timeout: 10 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()

	if resp.StatusCode < 200 || resp.StatusCode >= 300 {
		return fmt.Errorf("webhook delivery failed with status %d", resp.StatusCode)
	}
	return nil
}

func (e *LLMGatewayEvaluator) GetAuditLogs() []AuditLog {
	return e.AuditLogs
}

The SubmitEvaluation method executes the full cycle: payload transmission, latency measurement, validation pipeline execution, atomic metric tracking, webhook synchronization, and audit log generation. The SyncWebhook function delivers events to external ML monitoring platforms.

Complete Working Example

The following script combines all components into a runnable evaluation workflow. Replace the placeholder credentials and URLs before execution.

package main

import (
	"fmt"
	"log"
	"evaluator"
)

func main() {
	baseURL := "https://api.mypurecloud.com"
	token := "YOUR_OAUTH_ACCESS_TOKEN"
	webhookURL := "https://your-ml-monitoring-platform.com/api/webhooks/genesys-evals"

	evalClient := evaluator.NewLLMGatewayEvaluator(baseURL, token, webhookURL)

	rubrics := []evaluator.RubricDirective{
		{Name: "relevance", Weight: 0.4, Alignment: "auto", Threshold: 0.8},
		{Name: "coherence", Weight: 0.3, Alignment: "auto", Threshold: 0.85},
		{Name: "factual_accuracy", Weight: 0.3, Alignment: "strict", Threshold: 0.9},
	}

	groundTruth := map[string]string{
		"product_return_policy": "items may be returned within 30 days",
		"support_hours": "monday to friday 9am to 5pm",
	}

	payload, err := evaluator.BuildEvaluationPayload("prompt-gen-2024-11", groundTruth, rubrics, 10)
	if err != nil {
		log.Fatalf("Payload construction failed: %v", err)
	}

	result, err := evalClient.SubmitEvaluation(payload)
	if err != nil {
		log.Fatalf("Evaluation submission failed: %v", err)
	}

	fmt.Printf("Evaluation ID: %s\n", result.EvaluationID)
	fmt.Printf("Overall Score: %.2f\n", result.OverallScore)
	fmt.Printf("Success Rate: %.2f%%\n", evalClient.Tracker.GetSuccessRate()*100)
	fmt.Printf("Avg Latency: %.2fms\n", evalClient.Tracker.GetAverageLatencyMS())

	for i, audit := range evalClient.GetAuditLogs() {
		fmt.Printf("Audit Log %d: %s | Webhook: %s\n", i+1, audit.Timestamp, audit.WebhookStatus)
	}
}

Common Errors & Debugging

Error: 400 Bad Request

• What causes it: The evaluation payload violates schema constraints, exceeds the maximum metric count, or contains malformed JSON.
• How to fix it: Verify that len(ScoringRubric) <= MaxMetrics. Ensure all rubric weights fall between 0 and 1. Validate that promptId matches an active prompt in your Genesys Cloud environment.
• Code showing the fix: The Validate method in Step 1 explicitly checks these constraints before transmission.

Error: 401 Unauthorized or 403 Forbidden

• What causes it: The OAuth token has expired, or the client lacks the ai:llm-gateway:manage scope.
• How to fix it: Regenerate the token using the auth.OAuthClient implementation. Confirm the OAuth client configuration includes both ai:llm-gateway:manage and ai:evaluation:read scopes.
• Code showing the fix: The GetValidToken method automatically refreshes tokens before expiration. The SubmitEvaluation method checks status codes and returns descriptive errors.

Error: 429 Too Many Requests

• What causes it: The LLM Gateway API enforces rate limits per tenant and per OAuth client.
• How to fix it: Implement exponential backoff. The SubmitEvaluation method detects 429 responses and returns early. Wrap the call in a retry loop with time.Sleep delays.
• Code showing the fix: Add a retry wrapper that catches the 429 error and delays subsequent attempts.

Error: Validation Pipeline Rejection

• What causes it: The model output triggered bias detection or hallucination flags, or score format verification failed.
• How to fix it: Review the ValidationFlags in the response. Adjust your prompt template or ground truth matrix to reduce hallucination triggers. Ensure rubric thresholds align with your quality standards.
• Code showing the fix: The RunValidationPipeline function returns explicit error messages indicating which flag failed.

Official References

• Genesys Cloud LLM Gateway Evaluations API
• Genesys Cloud OAuth 2.0 Documentation
• Genesys Cloud Go SDK Reference
• Genesys Cloud AI Evaluation Webhooks