Retrieval-Augmented Generation (RAG) is essential for delivering accurate, enterprise-aware GenAI outputs. Applied RAG Architectures & Knowledge Grounding focuses on designing systems that reliably connect LLMs to trusted organizational data.
The course explores end-to-end RAG patterns, including data ingestion, embedding strategies, retrieval optimization, and response validation across Azure and multi-cloud environments. Participants learn how to reduce hallucinations, manage data freshness, and apply governance controls.
By the end of the course, learners are equipped to design grounded GenAI systems that deliver consistent, explainable, and enterprise-relevant results across knowledge-intensive use cases.
By the end of this course, participants will have the leadership toolkit to shape and steer GenAI portfolios across their organization.
Strategic Application
Bloom-aligned objectives
- Understand: why grounding is mandatory for enterprise reliability
- Analyze: RAG failure modes (irrelevant retrieval, hallucination, stale answers, injection via documents)
- Design: an end-to-end target architecture with explicit boundaries and contracts
Topics
- RAG system anatomy: ingestion, indexing, retrieval, generation, citations, feedback loop
- Grounding contract:
- evidence thresholds (top-k, min citations, min confidence)
- refusal and escalation policy (“no answer”, “needs human review”)
- traceability requirements (doc_id/chunk_id/uri/snippet spans)
- Architecture patterns:
- classic single-pass RAG
- hybrid + rerank RAG
- multi-hop and query-decomposition RAG
Labs
- Lab 1.1: Solution blueprint — Draft a reference architecture and component diagram for a chosen assistant (policy/QMS/tech-doc KB).
- Lab 1.2: Grounding contract pack — Define citation rules, evidence minimums, and “insufficient evidence” templates.
Bloom-aligned objectives
- Apply: chunking strategies aligned to retrieval and citations
- Create: an ingestion pipeline that produces high-quality, provenance-rich chunks
- Analyze: chunk quality issues and their downstream impact
Topics
- Extraction/normalization: PDFs, HTML, Office docs; de-duplication; boilerplate removal
- Chunking design:
- structure-aware chunking (headings/sections) vs fixed-size
- overlap tradeoffs, snippet extraction readiness, citation-friendly chunk IDs
- Metadata enrichment:
- source uri/title, section path, timestamps, business taxonomy, access tags/tenant IDs
- Embeddings strategy (model choice considerations, cost/latency tradeoffs, batching)
- Incremental refresh patterns: delta ingestion, tombstoning, versioning
Labs
- Lab 2.1: Ingestion job — Build extract → clean → chunk → enrich → embed → index pipeline.
- Lab 2.2: Chunk QA harness — Implement automated checks (length distribution, overlap, missing metadata, duplicate chunks).
- Lab 2.3: Freshness drill — Implement delta updates and validate that retrieval reflects updated sources correctly.
Bloom-aligned objectives
- Design: index schema for hybrid + filtered retrieval
- Implement: hybrid queries and ranking strategies
- Evaluate: relevance improvements from RRF + semantic ranker
Topics
- Hybrid search overview: executing text + vector in parallel and merging results with RRF
- RRF behavior: why rank fusion is used and how it impacts final ordering
- Index schema:
- searchable vs filterable vs facetable metadata
- vector fields, chunk fields, provenance fields
- ACL/tenant filtering patterns (filter-first retrieval)
- Semantic ranker:
- what it does, when to use it, limitations
- semantic captions/answers to improve snippet quality and citations
- Query strategy:
- query rewriting, expansion, metadata filters
- top-k sizing, reranking windows, “recall then precision” approach
Labs
- Lab 3.1: Search index build — Create an index schema with vectors + metadata + ACL tags; load sample corpus.
- Lab 3.2: Hybrid retrieval implementation — Implement keyword-only vs vector-only vs hybrid; inspect result sets and scores.
- Lab 3.3: Semantic rerank tuning — Enable semantic ranker and compare relevance, caption quality, and citation usefulness.
Bloom-aligned objectives
- Apply: grounded prompting patterns and output contracts
- Create: retrieval → synthesis → citation assembly workflow
- Evaluate: groundedness and citation correctness at runtime
Topics
- Grounded prompting patterns:
- evidence-first synthesis
- quote-and-cite
- “insufficient evidence” refusal route
- Response schema design:
- answer + citations array + rationale/limitations
- required fields per citation (doc uri, title, chunk_id, snippet span)
- Orchestration using prompt flow in Microsoft Foundry:
- chaining prompts with Python tools (retrieval client, post-processors)
- variants and iterative debugging in Foundry portal
Labs
- Lab 4.1: Grounded response pipeline — Build a prompt flow that retrieves, formats evidence, generates answer, and emits citations.
- Lab 4.2: Citation validator step — Add a validation node that fails outputs with missing/empty/irrelevant citations and routes to “no answer.”
Bloom-aligned objectives
- Understand: when single-pass retrieval fails
- Implement: iterative retrieval (re-query, query decomposition, multi-hop)
- Design: bounded agentic retrieval with safe tool execution
Topics
- Agentic retrieval patterns:
- query decomposition (sub-questions)
- multi-hop retrieval with stopping criteria
- clarification question vs re-retrieve decisioning
- Foundry Agent concepts for stateful interactions:
- threads, runs, messages for managing conversation state
- Microsoft Agent Framework integration (where a structured agent layer is needed)
- Safety boundaries:
- tool allowlists
- maximum retrieval iterations
- evidence thresholds per hop
Labs
- Lab 5.1: Multi-hop retrieval — Implement query decomposition + iterative retrieval with a maximum-hop policy.
- Lab 5.2: Agentic vs classic comparison — Compare answer quality and grounding between single-pass and iterative retrieval flows using the same evaluation set.
Bloom-aligned objectives
- Evaluate: quality with offline test sets and consistent metrics
- Analyze: failure clusters and root causes
- Create: promotion gates for retrieval and prompt changes
Topics
- Foundry evaluation runs:
- batch evaluation methods and interpreting results
- Custom evaluation flows:
- task-specific groundedness, citation correctness, retrieval relevance
- Dataset strategy:
- golden set creation (questions + expected sources)
- adversarial set (injection attempts, ambiguous queries, stale content)
- Iteration levers:
- chunking changes, index schema, filters, hybrid parameters, semantic config, prompt contract
Labs
- Lab 6.1: Golden dataset build — Create an evaluation dataset with expected citations and failure labels.
- Lab 6.2: Foundry evaluation gate — Run baseline vs improved configs and document go/no-go thresholds.
Bloom-aligned objectives
- Apply: defenses against prompt injection and data exfiltration
- Design: data boundary enforcement in retrieval
- Evaluate: system behavior under adversarial inputs
Topics
- Indirect prompt injection via documents (malicious instructions embedded in sources)
- Data boundary enforcement:
- ACL/tenant tag filters and “filter-first retrieval”
- citation redaction rules for sensitive sources
- Safe-fail policies:
- refuse when evidence is weak
- escalate to human review for high-risk requests
- Adversarial testing playbook:
- jailbreak/injection prompt packs
- retrieval poisoning scenarios
Labs
- Lab 7.1: Injection simulation — Test indirect injection documents and implement mitigations (instruction hierarchy + validator gates).
- Lab 7.2: Access control drill — Validate that retrieval never returns unauthorized chunks under multi-tenant filters.
Bloom-aligned objectives
- Analyze: latency and cost drivers in RAG
- Implement: practical optimizations without losing groundedness
- Create: an operational checklist for deployment readiness
Topics
- Latency budget: retrieval + rerank + generation
- Cost levers:
- reduce tokens via tighter context selection
- narrower retrieval with filters
- caching of retrieval results where safe
- Ops checklist:
- monitoring signals (retrieval quality drift, citation failures, error rates)
- release discipline for prompts/indexes/evaluation gates
Labs
- Lab 8.1: Performance drill — Measure baseline latency and implement one retrieval optimization and one prompt/context optimization; document impact.
- Lab 8.2: Production readiness checklist — Create a runbook (SLOs, dashboards, incident types, rollback plan).
Tools and platforms used
- Microsoft Foundry: prompt flow, variants, debugging, evaluation runs, custom evaluation flows
- Azure AI Search: vector search, hybrid search (RRF), semantic ranker, semantic captions/answers
- Azure OpenAI: embeddings + chat generation (grounded synthesis)
- Optional (as needed for agentic retrieval integration): Microsoft Agent Framework with Foundry Agents service
Organizational Impact
Engineering-Led RAG Design
Focus on retrieval, grounding, and evaluation as core system components.
Hybrid & Multi-Cloud Patterns
Transferable RAG architectures applicable across Azure and other cloud platforms.
High Hands-On Ratio
Pipelines, tuning drills, evaluation runs, and hardening exercises.
Enterprise-Grade Reliability
Built-in focus on security, safe failure modes, observability, and operations.
hands-on learning model
Access to sanitized process maps, KPI definitions, candidate initiative lists, and basic cost baselines (time, cycle time, error or rework rates)
Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.
One of our specialists will contact you within one business day.
Asked Questions
Find details on duration, delivery formats, customization options, and post-program reinforcement.
No. The course focuses on retrieval, grounding, and system reliability rather than model fine-tuning.
Yes. Evaluation-driven iteration is a core theme across multiple modules.
Yes. The course is designed for teams building and maintaining enterprise knowledge assistants and grounded copilots.
Approximately 70% of the course consists of hands-on labs, tuning drills, and evaluation exercises.
Cognixia delivers this course with a strong grounding-first and evaluation-driven philosophy, ensuring RAG systems are engineered for enterprise reliability rather than demo performance.
Participants work on realistic pipelines that include ingestion, retrieval, grounded generation, evaluation harnesses, and operational dashboards—mirroring real production environments.
Enterprise constraints such as access control, data boundaries, injection resistance, observability, and cost discipline are embedded throughout the learning journey, not treated as afterthoughts.
With deep experience in AI, data, and cloud transformation programs, Cognixia enables organizations to operationalize RAG capabilities with confidence and control.
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