by TensorHub Technologies
Programs / Evaluation & Observability
Quality & Reliability

Evaluation & Observability

Build the evaluation harnesses, tracing pipelines, and telemetry systems that catch hallucinations, regressions, and silent failures before your customers do. The discipline that turns "it worked in the demo" into "it works every day in production."

Request the syllabus See the curriculum
Duration
10 Weeks
Commitment
8–10 hrs/wk
Format
Live + Self-paced
Level
Intermediate
Curriculum

From ad-hoc testing to continuous evaluation

1

Foundations of LLM & Agent Evaluation

Weeks 1–3
  • Evaluation metrics: accuracy, groundedness, relevance, toxicity
  • Building golden datasets and test suites
  • LLM-as-judge and human-in-the-loop evaluation design
  • Benchmarking models and prompts systematically
2

Tracing & Telemetry

Weeks 4–5
  • Instrumenting agent pipelines with OpenTelemetry
  • Tracing with Langfuse, LangSmith, and Arize Phoenix
  • Cost and latency telemetry per agent step
  • Building observability dashboards in Grafana
3

Hallucination & Failure Detection

Weeks 6–8
  • Detecting hallucinations and grounding failures at scale
  • Drift detection for prompts, models, and data
  • Regression testing for agentic workflows
  • Alerting and incident triage for AI-specific failures
4

Continuous Improvement & Capstone

Weeks 9–10
  • Experiment tracking and A/B testing for prompts and agents
  • Feedback loops from production back into evaluation sets
  • Capstone: build a full evaluation & observability stack for a live agent
Outcomes

What you'll be able to do

  • ✓Design and run rigorous evaluation suites for LLMs and agents
  • ✓Instrument agent pipelines with full tracing and telemetry
  • ✓Detect hallucinations, drift, and regressions before customers do
  • ✓Build dashboards that make AI system health visible to any stakeholder
  • ✓Run continuous improvement loops from production feedback
Real-World Use Cases

Built on enterprise scenarios

Legal & Professional Services

Citation-accuracy evaluation harness

Automated evaluation pipeline that flags unsupported or fabricated citations in a legal research agent.

Financial Services

Real-time drift monitoring

Telemetry stack that detects when a market-analysis agent's output quality degrades after a model update.

Customer Experience

Support-agent quality dashboard

Live observability dashboard tracking resolution accuracy, tone, and escalation rates across support agents.

Job Market

Compensation & demand

Indicative ranges based on current AI quality/observability hiring patterns — actual compensation varies by experience, company, and geography.

RoleIndia (Annual)Global (Annual)
AI Evaluation Engineer₹18L – ₹50L$130K – $220K
AI Observability / Reliability Engineer₹25L – ₹65L$160K – $280K
Head of AI Quality₹55L – ₹1.2Cr+$240K – $400K+

One of the fastest-growing specializations as enterprises move from pilots to production and need provable reliability, not just capability.

✓

Designed and delivered by a Claude Architect–certified Forward Deployed Engineer

Every evaluation and observability pattern in this program was used to keep a real production agent system reliable — not built for a slide deck.

Claude Architect Certified Former Forward Deployed Engineer Production Reliability Experience
FAQ

Common questions

Which tools does this program cover?

Hands-on labs use Langfuse, LangSmith, Arize Phoenix, and OpenTelemetry, with dashboarding in Grafana. Concepts transfer to any evaluation/observability stack.

Is this only for LLM outputs, or agents too?

Both. The program covers single-turn LLM evaluation as well as multi-step agent trajectory evaluation, which requires different tracing and scoring approaches.

Catch failures before your customers do.

Get the full syllabus and a sample evaluation harness.

Request the syllabus Compare other programs