AI Evaluation & LLMOps
“Replace impressive demos with evidence that AI systems perform safely and consistently.”
Production AI requires a measurement system that is broader than a model leaderboard. An LLM application can fail through poor retrieval, unsupported answers, incorrect tool calls, sensitive-data leakage, unstable integration or unpredictable unit cost even when the base model scores well on public benchmarks. DataAIAdvisor helps organizations design evaluation datasets, stage gates, tracing, monitoring and review workflows that tie technical quality to the actual business task and risk tier.
What Decisions We Help You Make
Which offline and online tests credibly represent actual user tasks, multilingual content, exceptions and high-risk failure cases?
How should retrieval, answer, model reasoning behavior, tool execution, safety, reliability and economics be evaluated separately?
Which metrics must pass hard thresholds before promotion, and which require contextual human judgment?
How do teams detect regressions when prompts, models, knowledge, connectors or policies change?
Operational Signals & AI Friction
AI pilot demonstrations look impressive to leadership, but the team lacks a labeled test dataset, expert scoring rubrics, or reproducible benchmarks.
Project teams claim '98% accuracy' without disclosing the test population, sample size, failure criteria, or confidence intervals.
Prompt tweaks or foundation model version bumps introduce silent regressions that are only discovered when end users complain.
Application logs either expose unredacted sensitive customer PII or lack sufficient trace identifiers to diagnose failure causes.
What We Address / Workstreams
Evaluation Taxonomy & Metric Definition
Define task completion, retrieval recall/precision, grounded answer quality, citation fidelity, instruction following, structured output validity, and escalation accuracy.
Golden Test Dataset Engineering
Curate representative test sets with complex edge cases, multilingual variations, privileged queries, adversarial prompts, and domain expert ground truth.
Hybrid Evaluation Methodology
Combine automated deterministic checks, calibrated LLM-as-a-judge with human alignment sampling, expert domain rubrics, and uncertainty measurement.
Adversarial Red-Teaming & Security Testing
Test resilience against prompt injection, jailbreaking, data exfiltration, system prompt extraction, memory poisoning, and unauthorized tool invocation.
Performance Profiling & Unit Economics
Benchmark p50/p95/p99 latency, token utilization, cache hit rates, retry behavior, and cost per successfully resolved task rather than cost per raw token.
LLMOps Release Gates & CI/CD
Integrate evaluation suites into automated deployment pipelines, canary deployments, shadow traffic testing, and instant rollback procedures.
Production Observability & Feedback Loops
Monitor live query distribution shifts, source knowledge staleness, user thumbs-up/down feedback, complaint rates, and quality incident reviews.
Retrieval Evaluation
Test Suite 01- Recall@k & Precision@k
- Document ACL Permission Filters
- Stale & Expired Document Tests
Generation & Grounding Quality
Test Suite 02- Citation Provenance Fidelity
- Unsupported Assertion Rate
- Calibrated Refusal Accuracy
Security & Red-Team Harness
Test Suite 03- Direct & Indirect Injection Defense
- PII & Sensitive Data Leakage Scans
- Unauthorized Tool-Call Sandboxing
Unit Economics & CI/CD Gates
Test Suite 04- p95 Latency & Timeout Budgets
- Cost per Valid Resolved Task
- Automated Regression Release Gates
How We Approach It
A multi-tier evaluation framework connecting technical model behavior to verifiable operational performance and business risk gates.
Task & Failure Taxonomy
Define the specific business task, acceptable variance, and non-negotiable failure categories (e.g. data leak, false citation).
Golden Dataset & Rubric Creation
Construct a versioned, representative test set covering typical tasks, edge cases, and adversarial scenarios with expert rubrics.
Isolated Component & End-to-End Testing
Evaluate retrieval performance independently from generation synthesis and tool calling accuracy.
Release Quality Gates (CI/CD)
Establish automated regression test suites that block deployment if quality, safety, or latency thresholds are breached.
Production Tracing & Human Sampling
Operationalize continuous telemetry, calibrated human audit sampling, and structured feedback loops.
Strategic Trade-offs in Practice
Bilingual HR Policy Assistant Evaluation: Global Benchmarks vs. Local Golden Sets
Context: An enterprise deploys an internal HR assistant in both English and Vietnamese. The vendor provides public MMLU benchmark scores as proof of model readiness.
Public model leaderboards do not establish enterprise fitness; deployment authorization must be grounded in domain-specific, localized test datasets evaluated by internal subject matter experts.
Advisory Perspective & Trade-off:A generic global benchmark is not sufficient evidence of local enterprise fitness.
Tool-Calling Agent Version Upgrade Causing Silent Regressions
Context: An engineering team upgrades their underlying LLM foundation model to a newer version. Responses appear subjectively faster, but the agent starts duplicating support ticket creations.
Gate all model and prompt version updates on end-to-end transaction correctness and tool abuse testing, not text similarity or conversational fluency.
Advisory Perspective & Trade-off:Gate changes on end-to-end transaction correctness and abuse cases, not just text answer similarity.
Typical Deliverables
AI / LLM Evaluation Strategy & Metric Taxonomy
Framework defining evaluation dimensions, scoring rubrics, acceptance thresholds, and risk-tier alignments.
Versioned Golden Test Dataset Specification
Curated repository of representative, edge-case, and red-team test prompts with verified ground truth and labeling rubrics.
CI/CD Regression & Quality Gate Suite
Automated test harness configuration for pre-deployment regression verification, canary testing, and rollback triggers.
LLMOps Observability & Tracing Architecture
Specification for privacy-safe distributed tracing, token cost allocation, latency monitoring, and human review sampling.
Outcomes & Success Indicators
Quantitative evaluation metrics across the LLM application lifecycle:
- Task completion rate passing quality and grounding gates
- Evidence-supported response rate and unsupported assertion rate
- Retrieval recall@k and precision@k across domain test sets
- Tool-call execution accuracy and schema validation success rate
- Cost per valid, successfully resolved business task
Avoid reporting a single speculative 'hallucination percentage'; measure discrete failure modes across retrieval, synthesis, and tool execution.
Prerequisites & Safeguards
Evaluation principles and methodology limitations:
- LLM-as-a-judge is an automated screening assistant, not an absolute ground truth; it must be calibrated against human expert judgment.
- Public foundation model benchmarks do not prove production enterprise application fitness.
- System evaluation must encompass retrieval, tools, prompts, and permissions—not just model inference.
Questions We Frequently Hear
Q1.Can public LLM benchmarks certify that our enterprise application is ready for production?
No. Public benchmarks (like MMLU or HumanEval) evaluate raw foundation model capabilities on standardized academic tasks. Production fitness requires testing your specific data, proprietary workflows, internal permissions, multilingual requirements, and integration edge cases.
Q2.Is it safe to use another LLM to automatically evaluate our application outputs?
LLM-as-a-judge can provide valuable scale for regression testing, but it is subject to its own biases, blind spots, and non-determinism. It should always be calibrated against human expert evaluations, validated with clear rubrics, and supplemented by deterministic programmatic checks.
Q3.Does LLMOps monitoring only track model accuracy?
No. Comprehensive LLMOps observability monitors end-to-end workflow health, including retrieval latency, source knowledge freshness, API timeouts, token consumption, cost per resolved task, tool execution failures, user feedback signals, and security abuse attempts.
Related Capabilities & Solutions
Move AI from experimentation to enterprise value with architectural rigor.
Bring us your use-case dilemmas, data readiness gaps, RAG retrieval challenges, LLMOps evaluation questions, or AI governance concerns. We help design resilient architectures, production risk gates, and measurable business outcomes—without vendor bias or unverified claims.