Systems ship. Demos don't.
Where you are: the retrieval works, the tools fire, the demo lands. The gap: no evaluation set, no regression baseline, no pass/fail threshold — so nobody can say what happens on the next thousand queries. Where this takes you: you can state with numbers when a system is fit to release, and notice when it has drifted since. The path is Stage 3 of the Manifold ladder, Architecture & Judgement — four self-paced modules on how to evaluate LLM applications, RAG pipelines, and agentic workflows for hallucination, grounding, safety, latency, cost and drift.
Production systems fail for reasons demos never expose — hallucinations, weak grounding, retrieval failures, jailbreaks, memory bugs, tool-selection mistakes, runaway costs, latency spikes, and silent drift after deployment.
One prompt, one response, one happy reviewer. No evaluation set. No regression baseline. No proof the next 1,000 queries hold the same quality.
Hallucinations look like fluent answers. Stale retrieval looks like a clean response. Prompt injection looks like a normal request. Without evals, nothing surfaces.
No pass/fail thresholds. No regression check before deploy. No drift monitoring after deploy. "Ship it" becomes "hope it holds" — until production traffic proves otherwise.
Classical testing assumes deterministic behaviour. AI systems are probabilistic, stateful, and influenced by retrieval, memory, tools, and adversarial inputs — the whole testing stack has to be rebuilt around that reality.
By the end you will be testing the way these systems actually fail: evals over assertions, groundedness over greps, regression baselines over one-shot reviews, and a monitoring layer that survives production drift.
Agentic systems fail at different layers for different reasons. Testing them as one black box hides which layer is actually breaking.
Quality, correctness, hallucination, relevancy
Groundedness, faithfulness, precision, recall
Tool calls, state, memory, multi-step completion
Injection, jailbreaks, toxicity, data leakage
Latency, cost, regression, model + prompt drift
Ten things you can do afterwards — and defend in a room full of engineers — not ten topics the syllabus covers.
Test LLM responses for quality, correctness, hallucination, and relevance.
Validate grounding and faithfulness in RAG systems — including when the retrieved context is wrong.
Build evaluation datasets and benchmark cases that catch real failure modes.
Use metrics like precision, recall, MRR, NDCG, groundedness, faithfulness, relevancy — and know when each one matters.
Test multi-turn conversations and context retention across turns.
Validate agent workflows — state transitions, memory, tool calls, and multi-step completion.
Design red-team test cases for prompt injection, jailbreaks, and data leakage.
Build automated evaluation pipelines — rule-based, model-based, and LLM-as-judge.
Connect evaluation to CI/CD and release decisions — with pass/fail thresholds.
Monitor AI quality, drift, latency, cost, and reliability in production — not just in dev.
Every module is implementation-focused. Captured from live-taught sessions. Click any module for topics and the concrete outcome.
Selective by design. This works when you are already building — and want to prove, rather than hope, that what you built is ready.
Most AI evaluation content stops at "try this eval tool." You leave with judgement that survives the next tool, the next framework, and the production run after that.
Prompts are one input variable, not the system. We work the layer above — the evaluation, safety, and monitoring discipline that survives prompt changes.
We cover the patterns — LLM-as-judge, rule-based checks, golden datasets, regression baselines, drift monitors — so the thinking transfers to whichever tool your team uses.
LangChain, LangGraph, custom orchestrators — the testing model is the same. You learn how to test the system, not the SDK.
LLM apps, RAG, agents, safety, performance, monitoring — the four sessions stitch into one production-readiness model, not isolated topics.
Failure modes, pass / fail thresholds, release decisions, drift response — the calls that distinguish someone trusted with production from someone trusted with a notebook.
Helps you explain systems clearly in interviews and engineering discussions — with the right vocabulary for groundedness, drift, release readiness, and safety.
Templates, checklists and scorecards you can point at your own repositories on Monday — re-used across projects, not filed away after the course.
End-to-end checklist across LLM, RAG, agents, safety, and performance.
Golden-dataset structure — queries, ground-truth contexts, expected answers.
Coverage map for tool calls, state, memory, and multi-step completion.
Starter library of injection, jailbreak, and data-leakage probes.
Reference architecture for automated evaluation in CI/CD.
Pre-release pass/fail criteria across reliability, safety, and observability.
Post-release coverage — model, prompt, retrieval, and cost drift.
Template scorecard for groundedness, faithfulness, relevance, and safety.
Phrasings for evaluation, drift, and release decisions in real conversations.
Nachiketh has worked with AI / ML engineers, backend and DevOps practitioners, and senior learners across India and internationally — helping them move from tutorials to systems, from prototypes to production. This premium course distils the testing discipline used on real Agentic AI workloads into a structured 8-hour self-paced programme.
Stage 3 of the path — Architecture & Judgement. Self-paced, captured from live-taught sessions, lifetime portal access.
Premium self-paced programme. Questions about fit? See the FAQ below or email support@manifoldailearning.in.
If you have a question that's not here, email support@manifoldailearning.in.
You have the system. Add the layer that lets you say it is ready and show the evidence — evaluation, safety, and monitoring, applied to your own workloads.
You already bring real engineering experience. These live programs add the production layer on top of it — without asking you to start over.
Eight live weeks. One production-style Agentic AI system you build end to end — orchestration, governed tools & MCP, production RAG, async execution, evaluation, security, deployment — and every decision something you can defend. Nothing else required first: Python and LangChain foundation bonuses included free.
Secure Your Seat →Once you can ship the system, the harder question is which system to build. Discovery, scoping, an architecture you can defend, evaluation, delivery and adoption — twelve weeks of live case labs.
Explore the Residency →