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AI Systems Design: RAG Pipelines and LLM Architecture · LearnSpace
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AI Systems Design: RAG Pipelines and LLM Architecture

Курс от Board Infinity
Средний≈ 17 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Design and prototype enterprise-grade AI solutions that create real business value. In this course, you’ll learn to distinguish when to use predictive ML versus generative AI, align AI initiatives with business outcomes, and define success criteria that balance accuracy, latency, safety, and cost. You’ll compare traditional deterministic software with probabilistic AI systems to understand where AI is appropriate—and where it isn’t. You’ll diagram modern AI system architectures and evaluate build-versus-buy decisions for key components such as models, vector databases, and orchestration layers. Through hands-on work, you’ll implement data ingestion pipelines, chunking and embedding strategies, and retrieval flows, and you’ll prepare messy, unstructured enterprise data for use in AI systems. You’ll analyze orchestration patterns including tools, chains, and agents and learn when to apply each. The course culminates in building an end-to-end retrieval-augmented generation (RAG) prototype with an interactive Streamlit UI. You’ll experiment with cost–quality trade-offs, compare RAG with fine-tuning for different use cases, and use logs to iteratively test and refine your MVP. By the end, you’ll be able to demonstrate both technical viability and business feasibility for AI solutions within an enterprise context. Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

Навыки, которые вы освоите

AI Product StrategyEmbeddingsData PreprocessingVector DatabasesAI OrchestrationFine-tuningRetrieval-Augmented GenerationArtificial IntelligenceLarge Language ModelingSoftware ArchitectureAgentic systemsUnstructured DataFeasibility StudiesAI WorkflowsLLM ApplicationGenerative AI AgentsSystems ArchitectureGenerative AIUser Interface (UI)Solution Architecture

Программа курса

4 модулей · 64 учебных материалов

01Problem Formulation & AI Solution Scoping16 материалов

Career Scope + Predictive ML vs Generative AI

Career Scope in AI Systems ManagementВидеоPredictive ML vs Generative AI: Technical OverviewВидеоDecision Framework for Selecting the Right ParadigmВидео“ML vs GenAI in Modern Enterprise Architecture”Чтение

Framing AI Problems With Business Metrics

Учитесь у экспертов

Board Infinity

Instructor

AI Systems Design: RAG Pipelines and LLM Architecture
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 17 ч

4 модулей

Язык: Английский

Часть программы вашего университета
KPIs, ROI, and Acceptance CriteriaВидео
Translating Business Needs into AI RequirementsВидео
Constraints and FeasibilityВидео
“AI Requirements Template + KPI Examples”Чтение
Practice Quiz : Framing AI Problems With Business MetricsЗадание

Deterministic Software vs Probabilistic AI

Nondeterministic Outputs & UX ImplicationsВидеоTesting & Monitoring for Probabilistic SystemsВидеоExplaining AI Behavior to StakeholdersВидео“Failure Modes of Probabilistic Systems”ЧтениеPractice Quiz : Deterministic Software vs Probabilistic AIЗаданиеNew AssignmentЗаданиеQuick Course Check-InPLUGIN
02The Modern AI Stack: Models, Orchestration, Vector Databases16 материалов

Understanding the AI Stack

Components of the AI Stack (LLMs, Vector Stores, Orchestrators)ВидеоData Flow: Ingestion → Embedding → Retrieval → GenerationВидеоRetrieval Pipelines in Real SystemsВидео“Reference Architecture for Enterprise RAG Systems”ЧтениеPractice Quiz : Understanding the AI StackЗадание

Vector Databases & Embeddings

Embeddings Basics & Model TypesВидеоChunking Strategies & Retrieval OptimizationВидеоChromaDB & FAISS: Practical OperationsВидео“Embedding Model Comparison + Benchmark Tables”ЧтениеPractice Quiz : Vector Databases & EmbeddingsЗадание

Orchestration Frameworks (LangChain / LlamaIndex)

Chains, Tools, and Agent PatternsВидеоMulti-Step Reasoning & Tool UseВидеоSimple RAG Chain ImplementationВидео“Agentic Workflow Patterns: When to Use What”ЧтениеPractice Quiz : Orchestration Frameworks (LangChain / LlamaIndex)ЗаданиеGraded AssessmentЗадание
03Data Strategy for RAG and Fine-Tuning16 материалов

Chunking & Embedding Strategies

Chunking Heuristics (Size, Overlap, Structure)ВидеоEmbedding Model Selection (Dimensionality, Cost)ВидеоMeasuring Retrieval AccuracyВидео“Chunking Playbook + Evaluation Notebook”ЧтениеPractice Quiz : Chunking & Embedding StrategiesЗадание

RAG vs Fine-Tuning

RAG ArchitecturesВидеоFine-Tuning ArchitecturesВидеоDecision Matrix: Costs, Latency, SensitivityВидео“Cost Comparison Case Studies: RAG vs FT”ЧтениеPractice Quiz : RAG vs Fine-TuningЗадание

Preparing Enterprise Data

Cleaning & Normalization TechniquesВидеоMetadata Tagging for RetrievalВидеоData Readiness Risks (Noise, PII, Gaps)Видео“Enterprise Data Readiness Checklist”ЧтениеPractice Quiz : Preparing Enterprise DataЗаданиеNew AssignmentЗадание
04Prototyping & Building the Company Brain16 материалов

Building the Retrieval Pipeline

End-to-End RAG System OverviewВидеоImplementing Ingestion → Chunking → EmbeddingВидеоRetrieval Quality ValidationВидео“RAG Debugging Guide”ЧтениеPractice Quiz : Building the Retrieval PipelineЗадание

Streamlit for AI Prototypes

Streamlit for AI PrototypesВидеоIntegrating Retrieval Chain into UIВидеоDeploying the MVPВидео“Streamlit UI Patterns for Chatbots”ЧтениеPractice Quiz : Streamlit for AI PrototypesЗадание

Testing & Refining the MVP

Identifying Hallucinations & Irrelevant RetrievalsВидеоFixing Latency BottlenecksВидеоOptimizing Grounding & Output QualityВидео“MVP Troubleshooting Manual”ЧтениеPractice Quiz : Testing & Refining the MVPЗаданиеRun tests → observe logs → propose fixes.Задание