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AI Evaluation & Guardrails: Production-Grade Systems
New
2 students
AI Evaluation & Guardrails: Production-Grade Systems
Golden datasets, LLM judges, guardrails, red-teaming and release gates — proof your AI is ready to ship
Created byNaveen Kumar Josyula
Last updated 11/2026
English
What you'll learn
- Build a golden-set harness, rubric judge, drift watcher, guardrail pipeline, red-team suite and release gate yourself, on screen, and watch each run on real cas
- Check out any hands-on lesson's exact starting code from the companion repo, build along with the video, and verify your result against its finished checkpoint
- Read real evaluation output — scores, logs, breaches and blocked releases — and explain what each result proves and what it does not
- Explain why demos fail as evidence and what a defensible evaluation claim looks like
- Decompose quality into correctness, faithfulness, safety, latency and cost — and measure each separately
- Design golden datasets from real failures, with labels and provenance a sceptic can audit
- Score systems on task success, tool correctness and trajectory quality as separate axes
- Choose exact, fuzzy and semantic scoring to match the shape of the answer
- Measure retrieval and RAG systems layer by layer: precision, recall, faithfulness
- Write rubrics whose dimensions grade independently, with anchored scales
- Detect and correct LLM-judge failure modes: verbosity bias, position bias, self-preference
- Calibrate a judge against human labels — and know when a judge may annotate but never gate
- Size an evaluation set from the decision it must support, and report scores with Wilson intervals
- Compute Cohen's kappa and act on what grader disagreement is really telling you
- Version datasets like code, and detect label and concept drift before users do
- Design human review queues, annotation guidelines and calibration sessions that scale
- Build input and output guardrails with a complete, auditable decision log
- Threat-model an AI feature and design adversarial cases with expected safe outcomes
- Measure refusal quality in both directions — breaches and false refusals
- Sample and score production traffic, and set alert thresholds with named owners
- Wire evaluation gates into CI/CD that block regressions and name their reasons
- Close the incident loop: every production failure becomes a permanent test case
- Defend a complete evaluation system in a production readiness review
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Course content
13 sections • 51 lectures • 8h 32m total length
Requirements
- Working knowledge of Python (you can read and write small programs; every demo is standard-library only)
- You have built or operated at least one LLM application — a chatbot, a RAG pipeline or an agent — and felt the "how do we know it's good?" problem
- No prior evaluation, statistics or ML-ops experience is assumed; the statistics are taught from first principles
- A computer with Python 3.11+ to run the companion code repository
Description
This course contains the use of artificial intelligence.
This is a hands-on course, and the hands-on work is the point. In every practical lesson you watch the code being written on screen, step by step — and then you watch it run: the real output, the logs, and the failures that appear before the fixes. Each of those lessons comes with the working code in the companion repository, tagged at the exact start and finish of the lesson, so you can check out the starting point, build along with the video, and compare your result against the finished, verified version. Nothing here is narrated that was not first built and run.
Every team shipping AI this year faces the same meeting. The demo impressed everyone — and then someone senior asks the only question that matters: how good is it, on what evidence, and who measured it? A model can be brilliant and still unshippable, because production sign-off is not granted to demos. It is granted to systems whose quality has been measured honestly, whose failures have names, and whose owners can prove both on demand. Evaluation is that proof, and guardrails are what keep the proof true after launch day.
This course builds the complete evaluation system, layer by layer, in the order production teams actually need it. You will start with ground truth: golden datasets mined from real failures, labelled under versioned guidelines, and defended case by case. Then the measurement core — metrics for retrieval, generation and agents; LLM judges with rubrics that grade consistently and calibration that keeps them honest; and the statistics that separate a real improvement from a lucky sample — sample sizes, Wilson intervals and Cohen's kappa, computed by hand so no tool can bluff you.
Around that core, the disciplines that keep scores true over time. Datasets drift as users and policies change, so you will version them like code and watch for drift before your users report it. Human evaluation is treated as the scarce instrument it is: queues designed around risk, guidelines that survive contact with real annotators, hidden gold items that measure the measurers. Guardrails get their own honest treatment — runtime prevention is not measurement — as a pipeline of input and output checks whose every decision lands in an audit log. And because a friendly test set never attacked anyone, you will red-team the system, write down the breaches it finds, and wire the survivors into regression gates that block a worse release before your users meet it.
The second half turns evaluation into an operating discipline. Online evaluation samples real production traffic — catching the silent quality drop that offline scores and dashboard averages smooth away — and the incident loop graduates every production failure into a permanent test case inside 48 hours. The capstone assembles everything onto one system: golden set, harness, calibrated judge, guardrail pipeline, red-team report, online plan and release gate, defended in an evaluation readiness review that ends in a go or a no-go with the blockers named out loud.
Every mechanic ships as working, standard-library-only Python you can run offline with no API keys: a golden-set harness, a rubric judge, agreement statistics, a drift watcher, a human review round, a guardrail pipeline, a red-team suite, an online sampler, a CI release gate and a readiness review. You will not just hear that a control works — you will run it, watch it catch a real failure, and then recognise the same machinery inside the platforms (LangSmith, Galileo, RAGAS, DeepEval and their peers) that you will meet at work. This course is the measurement layer for the instructor's RAG Architecture, MCP Architecture, AI Security and AI Agent Frameworks courses, and it stands on its own for everyone else.
Who this course is for:
- AI and ML engineers who must prove quality before a system ships — and after
- Platform and LLMOps engineers building evaluation, guardrail and monitoring layers for AI teams
- Engineering and product leaders who sign off AI releases and need evidence they can defend
- Builders from this series' RAG, MCP, AI Security and Agent Frameworks courses whose systems now need a measurement layer
- Any team whose AI demo worked — and whose sign-off meeting did not