Diploma in LLM Systems & RAG Engineering
Production retrieval-augmented generation, multi-agent orchestration, evaluation harnesses, and enterprise LLM infrastructure.
Diploma in LLM Systems & RAG Engineering is a self-paced online advanced diploma in generative-ai from Altaris AI Academy. Tuition is $399 CAD (payable in three interest-free instalments), most students finish in about 6 months at 11 hours a week, and graduates receive a 18-credit, employer-verifiable diploma. 30-day money-back guarantee.
About this program
Master the engineering practices required to build, evaluate, and scale production LLM systems. Covers hybrid retrieval, reranking, chunking strategies, semantic caches, multi-agent graphs, and deterministic evaluation gates.
Who this program is for
- →Practitioners already shipping generative-ai work who want depth
- →Senior engineers, data scientists, and technical leads
- →Canadian residents seeking a verifiable diploma credential
Topics you'll cover
6 modules across 6 months — 29 lessons in total.
Full syllabus
Module 1 · Module 1: Advanced Ingestion, Chunking & Embeddings▾
- L1Parsing messy PDFs, tables, slides, and scanned forms with OCR pipelines
- L2Semantic, recursive, agentic, and late-chunking strategies
- L3Embedding model benchmarks: dense vs. sparse vs. multi-vector (ColBERT)
- L4Vector index topologies: HNSW, IVF-PQ, and partition indexing at scale
- L5Module 1 Capstone: Enterprise financial filing ingestion engine with table extraction
Module 2 · Module 2: Hybrid Retrieval & Reranking Engines▾
- L1Reciprocal Rank Fusion (RRF) and hybrid search architectures
- L2Cross-encoder reranking: Cohere, BGE, and custom transformer rerankers
- L3Query expansion, HyDE (Hypothetical Document Embeddings), and sub-query routing
- L4Document-level metadata filtering, access control (RBAC), and multi-tenancy
- L5Module 2 Capstone: Sub-second legal discovery engine over 100,000 regulatory documents
Module 3 · Module 3: Structured Outputs, Function Calling & Guardrails▾
- L1Constrained decoding, grammars, and JSON Schema schema enforcement
- L2Advanced tool calling with validation, schema fallback, and retries
- L3Input/output guardrails: NeMo Guardrails, LlamaGuard, and PII masking
- L4Prompt versioning, regression testing, and semantic caching architectures
- L5Module 3 Capstone: Automated banking transaction reconciliation agent with strict output schemas
Module 4 · Module 4: Multi-Agent Orchestration & State Graphs▾
- L1State machines and cyclic graph execution (LangGraph, AutoGen)
- L2Plan-and-solve vs. ReAct vs. reflexive agent architectures
- L3Long-term memory management: episodic, procedural, and working memory
- L4Human-in-the-loop approval workflows, timeouts, and rollback states
- L5Module 4 Capstone: Multi-agent software code audit and remediation system
Module 5 · Module 5: RAG Evaluation, Observability & Cost Engineering▾
- L1Ragas evaluation triad: Faithfulness, Answer Relevance, Context Precision
- L2Automated synthetic golden test set generation
- L3Full-stack LLM tracing with OpenTelemetry, Langfuse, and Arize Phoenix
- L4Prompt compression, token budget management, and model tier routing
- L5Module 5 Capstone: CI/CD evaluation gate blocking regressions in pull requests
Module 6 · Module 6: Capstone Practicum & Industry Defense▾
- L1System specification defense and threat modeling
- L2Production deployment with load testing and latency SLAs
- L3Verification of security, evaluation matrices, and cost model
- L4Live jury defense with senior Canadian AI engineering leaders
What you'll be able to do
- ●Design and deploy hybrid retrieval pipelines combining BM25, dense vectors, and cross-encoders
- ●Build autonomous multi-agent workflows with state machines, tool calling, and human-in-the-loop gates
- ●Establish automated LLM evaluation harnesses using Ragas, DeepEval, and deterministic synthetic test sets
- ●Implement token-level caching, prompt compression, and aggressive latency/cost optimization
- ●Structure strict JSON schema validation and structured output contracts
- ●Deliver Canadian enterprise-ready RAG with document-level RBAC and tenant isolation
Career paths after graduation
Frequently asked questions
How much does the Diploma in LLM Systems & RAG Engineering cost?▾
Tuition is $399 CAD, paid once by invoice: apply online, we review your application and email an invoice, and your program opens once it is paid. A 30-day refund window applies from your enrollment date.
How long is the Diploma in LLM Systems & RAG Engineering program?▾
Self-paced. Most students complete it in 6 months at roughly 7 hours per week, but you can go faster or slower — you keep lifetime access.
What are the prerequisites?▾
Strong Python or TypeScript backend development experience; Familiarity with REST APIs, asynchronous programming, and databases; Basic conceptual understanding of large language models and embeddings
What credential does the program award?▾
Graduates receive the Altaris AI Academy Diploma in generative-ai — a verifiable credential with a unique certificate number you can publish on LinkedIn and that any employer can verify at altarisai.org/verify. Employer acceptance is determined by each employer; it is not a regulated professional designation.
What is the refund policy?▾
Full refund within 30 days of enrollment, no questions asked. After day 30, prorated refunds are available per our Refund Policy.
Who teaches the program?▾
Working Canadian AI practitioners — not academics. Every module is built and reviewed by a lead instructor working in the field today.