APAC LLM Evaluation Guide 2026: Giskard, TruLens, and Confident AI

APAC LLM Quality Assurance: Vulnerability Scanning, RAG Evaluation, and Regression Testing

Shipping LLM applications to APAC production without systematic evaluation is the leading cause of APAC AI incidents — hallucinations in regulated advice, prompt injection in customer-facing chatbots, and RAG quality regressions after retrieval pipeline changes. This guide covers the evaluation tools APAC AI teams use to measure, gate, and continuously monitor LLM application quality before and after APAC production deployment.

Three tools address the APAC LLM evaluation lifecycle:

Giskard — open-source LLM vulnerability scanner using AI-generated adversarial probes across seven risk categories for APAC pre-production safety testing.

TruLens — open-source RAG evaluation framework measuring context relevance, groundedness, and answer relevance for APAC LLM quality assurance.

Confident AI — cloud LLM evaluation platform built on DeepEval with CI/CD regression testing quality gates and managed APAC dataset storage.

APAC LLM Evaluation Architecture

APAC LLM Quality Gates:

Pre-production (before shipping):
  New LLM feature → Giskard vulnerability scan → fix vulnerabilities → TruLens eval → Confident AI CI gate → deploy

Post-production (after shipping):
  Prod traffic sample → Confident AI monitoring → alert on quality regression → investigate with TruLens

Evaluation Responsibilities:
  Giskard:       Safety and security (what can go wrong?)
  TruLens:       RAG quality measurement (is retrieval+generation accurate?)
  Confident AI:  Regression prevention (is this version worse than last version?)

APAC RAG Triad (TruLens):
  Context Relevance:  Retrieved APAC docs contain information needed to answer
  Groundedness:       LLM answer is supported by retrieved APAC context (no hallucination)
  Answer Relevance:   LLM answer addresses the APAC user's actual question

  All three must be high — failure mode examples:
    Low Context Relevance:  retrieval returns wrong APAC docs → LLM answers from parametric memory
    Low Groundedness:       LLM adds information not in APAC context → hallucination risk
    Low Answer Relevance:   LLM gives accurate but off-topic APAC response → user confusion

Giskard: APAC LLM Vulnerability Scanning

Giskard APAC scan setup

# APAC: Giskard — automated vulnerability scanning for LLM applications

import giskard
from giskard import Model, Dataset

# APAC: Wrap any LLM application in a Giskard model
def apac_llm_predict(df):
    """APAC LLM application — one row per user query."""
    responses = []
    for _, row in df.iterrows():
        response = call_apac_llm(
            system=APAC_SYSTEM_PROMPT,
            user=row["user_query"],
        )
        responses.append(response)
    return responses

# APAC: Create Giskard model wrapper
apac_giskard_model = Model(
    model=apac_llm_predict,
    model_type="text_generation",
    name="APAC Enterprise Chatbot v2.3",
    description="APAC enterprise AI assistant for MAS-regulated financial services queries",
    feature_names=["user_query"],
)

# APAC: Create sample dataset for context-aware probe generation
apac_sample_data = giskard.Dataset(
    df=pd.DataFrame({
        "user_query": [
            "What are the MAS FEAT principles for AI governance?",
            "How do I calculate SGD interest rates for fixed deposits?",
            "What compliance documents do I need for AML reporting?",
        ]
    }),
    name="APAC Financial Services Queries",
    target=None,
)

# APAC: Run vulnerability scan — generates adversarial probes automatically
apac_scan_results = giskard.scan(apac_giskard_model, apac_sample_data)
print(apac_scan_results)
# APAC: Scan report shows:
# → Hallucination risk: MEDIUM (3 issues found)
# → Prompt injection risk: LOW (1 issue found)
# → Harmful content risk: LOW (0 issues found)
# → Stereotype bias risk: HIGH (5 issues found) ← requires investigation

Giskard APAC CI/CD integration

# APAC: Giskard — fail CI when vulnerability score exceeds threshold

import pytest

def test_apac_llm_safety():
    """APAC: CI gate — block deployment on safety regression."""
    apac_results = giskard.scan(apac_giskard_model, apac_sample_data)

    # APAC: Fail if any high-severity issues found
    apac_issues = apac_results.issues
    apac_high_severity = [i for i in apac_issues if i.importance == "high"]

    assert len(apac_high_severity) == 0, (
        f"APAC deployment blocked: {len(apac_high_severity)} high-severity vulnerabilities found:\n"
        + "\n".join([f"  - {i.category}: {i.description}" for i in apac_high_severity])
    )
    print(f"APAC safety check passed: {len(apac_issues)} total issues, 0 high severity")

TruLens: APAC RAG Quality Evaluation

TruLens APAC RAG triad setup

# APAC: TruLens — RAG evaluation with context relevance, groundedness, answer relevance

from trulens.apps.langchain import TruChain
from trulens.providers.openai import OpenAI as TruOpenAI
from trulens import Tru, Feedback, Select

apac_tru = Tru()
apac_provider = TruOpenAI(model_engine="gpt-4o-mini")  # APAC: cheap judge model

# APAC: Define RAG triad feedback functions
apac_context_relevance = (
    Feedback(apac_provider.context_relevance_with_cot_reasons, name="APAC Context Relevance")
    .on(Select.RecordCalls.retrieve.rets.rets[:])  # APAC: retrieved chunks
    .on_input()
    .aggregate(np.mean)
)

apac_groundedness = (
    Feedback(apac_provider.groundedness_measure_with_cot_reasons, name="APAC Groundedness")
    .on(Select.RecordCalls.retrieve.rets.rets[:].collect())
    .on_output()
)

apac_answer_relevance = (
    Feedback(apac_provider.relevance_with_cot_reasons, name="APAC Answer Relevance")
    .on_input_output()
)

# APAC: Instrument LangChain RAG chain
apac_tru_chain = TruChain(
    apac_rag_chain,
    app_name="APAC Knowledge Base RAG",
    app_version="v1.2",
    feedbacks=[apac_context_relevance, apac_groundedness, apac_answer_relevance],
)

# APAC: Run evaluation on APAC test queries
apac_test_queries = [
    "What are the PDPA requirements for AI data processing in Singapore?",
    "How does MAS regulate AI in retail banking?",
    "What are HKMA AI governance expectations for 2026?",
]

with apac_tru_chain as recording:
    for apac_query in apac_test_queries:
        apac_response = apac_rag_chain.invoke({"question": apac_query})

# APAC: View results dashboard
apac_tru.run_dashboard()
# → http://localhost:8501
# Shows: Context Relevance avg 0.82 | Groundedness avg 0.91 | Answer Relevance avg 0.88

TruLens APAC retrieval diagnosis

# APAC: TruLens — diagnose low context relevance in APAC RAG pipeline

# APAC: After TruLens evaluation, analyze low-scoring interactions
apac_records, apac_feedback_results = apac_tru.get_records_and_feedback(
    app_ids=["APAC Knowledge Base RAG"]
)

# APAC: Filter low context relevance (below 0.7)
apac_low_retrieval = apac_records[
    apac_feedback_results["APAC Context Relevance"] < 0.7
]

for _, row in apac_low_retrieval.iterrows():
    print(f"Query: {row['input'][:80]}")
    print(f"Context relevance: {row['APAC Context Relevance']:.2f}")
    print(f"Retrieved chunks: {row['retrieved_context'][:200]}")
    print("---")

# APAC: Common APAC diagnoses:
# Low context relevance: APAC embedding model not suited for CJK queries
#   → Fix: switch to jina-embeddings-v3 or multilingual-e5-large
# Low groundedness: LLM adds information beyond APAC context
#   → Fix: tighten system prompt to stay within context boundaries
# Low answer relevance: LLM answers adjacent APAC question, not the actual one
#   → Fix: improve APAC query understanding prompt

Confident AI: APAC LLM Regression Testing

Confident AI APAC CI/CD quality gates

# APAC: Confident AI — LLM regression testing with DeepEval + cloud dashboard

import deepeval
from deepeval import evaluate
from deepeval.metrics import (
    FaithfulnessMetric,
    AnswerRelevancyMetric,
    ContextualRecallMetric,
    HallucinationMetric,
)
from deepeval.test_case import LLMTestCase

# APAC: Configure Confident AI cloud (stores results, manages datasets)
deepeval.login_with_confident_ai_credentials(
    api_key=os.environ["CONFIDENT_AI_API_KEY"]
)

# APAC: Define evaluation metrics for APAC RAG use case
apac_metrics = [
    FaithfulnessMetric(threshold=0.8, model="gpt-4o-mini"),      # APAC: no hallucination
    AnswerRelevancyMetric(threshold=0.8, model="gpt-4o-mini"),   # APAC: on-topic answers
    ContextualRecallMetric(threshold=0.75, model="gpt-4o-mini"), # APAC: good retrieval
    HallucinationMetric(threshold=0.1, model="gpt-4o-mini"),     # APAC: low hallucination rate
]

# APAC: Build test cases from golden dataset
apac_test_cases = []
for apac_item in apac_golden_dataset:
    apac_response = run_apac_rag(apac_item["question"])
    apac_test_cases.append(LLMTestCase(
        input=apac_item["question"],
        actual_output=apac_response["answer"],
        retrieval_context=apac_response["context_chunks"],
        expected_output=apac_item["expected_answer"],  # APAC: golden answer
    ))

# APAC: Run evaluation — results pushed to Confident AI dashboard
apac_eval_results = evaluate(
    test_cases=apac_test_cases,
    metrics=apac_metrics,
    run_async=True,
)

# APAC: CI/CD gate — fail if any metric below threshold
for result in apac_eval_results:
    if not result.success:
        raise SystemExit(f"APAC deployment blocked: {result.metrics_data}")

print("APAC quality gate passed — all metrics above threshold")
# APAC: Results visible in Confident AI dashboard for APAC team review

Related APAC LLM Evaluation Resources

For the LLM observability tools (Arize Phoenix, AgentOps) that complement Confident AI by providing real-time production traces and span-level LLM monitoring — enabling APAC teams to sample production traffic for evaluation rather than relying solely on offline test datasets — see the APAC LLM observability guide.

For the LLM security tools (LLM Guard, Rebuff, Presidio) that address the security vulnerabilities Giskard surfaces — providing runtime blocking of prompt injection attacks and PII in APAC production rather than only detecting them in pre-production scans — see the APAC LLM security guide.

For the open-source LLM evaluation frameworks (promptfoo, DeepEval, Ragas) that provide self-hosted alternatives to Confident AI cloud for APAC teams with data sovereignty requirements, see the APAC LLM evaluation frameworks guide.