Take LLM systems from prototype to dependable production. This track covers prompt and context engineering, RAG pipelines, evaluation, monitoring and guardrails — the operational discipline of running AI at scale.
Shipping LLMs Without Ops Is Shipping Blind
LLM systems fail quietly — hallucination, retrieval drift, prompt injection and runaway token cost rarely announce themselves. Without evaluation, observability and guardrails, you can't tell a healthy system from a degrading one until your users — or attackers — do it for you. Production LLMs need operational discipline, not just a clever prompt.
A blend of applied AI-engineering fundamentals and hands-on operations your team can use immediately — every highlight below maps to a module in the detailed course syllabus.
Prompt & context engineering for reliable, repeatable behaviour
RAG pipelines & data — chunking, embeddings, retrieval quality and grounding
Evaluation & observability — measuring quality and catching drift in production
Guardrails & safety — prompt-injection defence, PII handling and output filtering
Cost, latency & scaling — running LLM services affordably and predictably
Three delivery depths — from a leadership briefing to a full hands-on lab — all tailored to your stack and use cases.
A leadership session on what LLMOps is, where AI projects stall and how to fund the discipline — no coding required.
For AI and platform engineers: RAG, evaluation, observability and guardrails, with guided demos on a working stack.
Full immersion: build a RAG pipeline, stand up an eval & monitoring harness, add guardrails and ship a capstone LLM service.
Conceptual familiarity with LLMs is enough for the executive briefing — no coding required
Comfort with Python and REST APIs for the technical deep-dive
Working knowledge of Python and APIs for the hands-on lab — no prior ML experience needed
A hosted LLMOps lab with starter RAG and evaluation repos — no local setup needed
An observability dashboard template, slide decks and quick-reference cards to keep
A pre-session readiness checklist so every attendee arrives at the right level
Content is pitched to each audience so builders, data teams and product leaders all leave with what they need.
Design and operate the LLM services the rest of the organisation builds on.
Leave able to architect RAG, evaluation and observability into production-grade systems.
Build the pipelines, data flows and APIs that feed and surround the model.
Leave able to engineer retrieval, embeddings and data quality for grounded, reliable answers.
Set quality bars, manage cost and own the reliability of AI features in production.
Leave with a framework for measuring quality, controlling spend and operating LLMs at scale.
Capabilities and tangible artifacts that translate directly into your AI engineering program.
A repeatable path from LLM prototype to reliable production deployment
RAG and context patterns that improve accuracy and reduce hallucination
Objective evaluation so quality is measured, not guessed
Operational control over cost, latency and reliability
A production LLM reference architecture you can adapt to your stack
An evaluation & monitoring harness for offline and online testing
A RAG pipeline blueprint covering chunking, embeddings and retrieval
A guardrails & cost-control checklist for safe, affordable operations
A QSECS certificate of completion for every participant
Five modules scaling from LLMOps foundations to a hands-on production capstone. Select a module to expand it.
The lifecycle of a production LLM system and where it differs from traditional MLOps and software delivery.
Tokens, context windows, temperature and non-determinism — the model properties that shape every design decision.
The components of a real LLM application: gateway, orchestration, retrieval, evaluation and observability.
Hallucination, drift, prompt injection and cost blow-outs — the risks LLMOps exists to control.
Structuring instructions, examples and roles for reliable, repeatable output.
Assembling the right context within the window — what to include, compress or leave out.
Function and tool calling, JSON modes and validating model responses against a schema.
Versioning, testing and rolling out prompt changes like any other production artifact.
Why retrieval grounds answers, and the anatomy of a retrieval-augmented pipeline.
Splitting documents and choosing embedding models for high-quality retrieval.
Indexing, hybrid search and re-ranking to put the right context in front of the model.
Ingestion pipelines, updates and provenance so the knowledge base stays current and trustworthy.
Curating representative cases and defining what "good" output means for your use case.
Automated scoring, LLM-as-judge and human review across pre-release and live traffic.
Logging prompts, retrievals and responses end-to-end to debug and explain behaviour.
Catching quality decay from data, prompt or model changes before users do.
Input/output filtering, PII handling and content controls for safe LLM applications.
The OWASP Top 10 for LLM Applications and practical mitigations against injection and data leakage.
Caching, model routing and token budgeting to keep services fast and affordable.
Build, evaluate and operate an end-to-end LLM service with guardrails and a monitoring dashboard.
Awarded by QSECS · Quantum Security Solutions
Every participant who completes the track receives a verifiable QSECS Certificate of Completion — a credible signal to leadership, customers and auditors that your teams can build and operate LLM systems responsibly.
Individually issued with a unique, verifiable credential ID
Hands-on and lab tracks include a graded capstone assessment
Maps to continuing-education (CPE) hours for common security certifications
Shareable to LinkedIn and your internal skills matrix
An illustrative Day 1 from the 2-day format — every agenda is tailored to your goals before delivery.
Why demos are easy and production is hard, and what operational discipline an LLM system actually needs.
Designing prompts and assembling context for reliable, repeatable behaviour — with worked examples.
Chunking, embeddings, vector search and re-ranking — improving retrieval quality and grounding.
Building an eval set, offline/online scoring and end-to-end tracing of prompts and retrievals.
Wire a small evaluation harness to a RAG pipeline and score its answers on the wire.
Day 2 covers guardrails and prompt-injection defence, cost and latency management, monitoring dashboards and a production-readiness workshop.
Everything teams usually ask before booking the LLMOps & AI Engineering track.
Conceptual familiarity with LLMs is enough for the executive briefing. The technical deep-dive and hands-on lab assume comfort with Python and REST APIs — no prior machine-learning experience is required. We send a readiness checklist beforehand so everyone arrives at the right level.
The track is provider-agnostic. We teach principles that transfer across hosted and open models and across common orchestration, vector-store and evaluation tooling, then adapt examples to the stack your team already uses.
All three. We run sessions in-person at your site, fully remote, or hybrid — across time zones for distributed teams. The hands-on labs run in a hosted environment so delivery mode never changes the experience.
We provide a hosted LLMOps lab pre-loaded with starter RAG and evaluation repos and an observability dashboard template. Participants only need a browser — there's no local setup, and nothing touches your production systems.
Yes. We tailor examples and labs to your stack, and can anchor the RAG and evaluation work to your real use cases and data under NDA. Tailoring is scoped during the requirement-analysis call.
Yes — every participant receives a verifiable QSECS Certificate of Completion, and hands-on tracks include a graded capstone. The credential maps to CPE hours for common security certifications.