Systems ship. Demos don't.
A premium self-paced course — captured from four live-taught sessions — on how to evaluate LLM applications, RAG pipelines, and agentic workflows for hallucinations, grounding, safety, reliability, latency, and production readiness — the evaluation layer most engineers are never taught.
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.
This accelerator teaches the testing model that actually fits agentic AI: 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
By the end of this accelerator, you'll be able to design the evaluation, safety, and monitoring layers that turn an AI prototype into a release-ready system.
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 accelerator works best when you're already building — and want to learn how to prove your systems are ready to ship.
Most AI evaluation content stops at "try this eval tool." This accelerator builds the engineering judgment that holds up across tools, frameworks, and production reality.
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.
Concrete templates and checklists you carry back into your own stack — designed to be re-used across projects, not just inside the cohort.
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.
A premium self-paced course — captured from live-taught sessions — with 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.
If you're done shipping demos and ready to learn the evaluation, safety, and monitoring layer that real production systems require — this is the course.