When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

RAG-Driven Generative AI

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Ce cours n'est pas disponible en Français (France)

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RAG-Driven Generative AI

Instructeur : Packt - Course Instructors

Inclus avec

10 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Avancées

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

10 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Avancées

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

Scale RAG pipelines to handle large datasets efficiently
Implement techniques that reduce hallucinations and improve response accuracy
Customize and scale RAG-driven AI systems across different domains

Compétences que vous acquerrez

Catégorie : Deep Learning
Catégorie : Artificial Intelligence
Catégorie : Natural Language Processing
Catégorie : Semantic Web
Catégorie : Scalability
Catégorie : Data Pipelines
Catégorie : Machine Learning
Catégorie : Data Science

Outils que vous découvrirez

Catégorie : OpenAI
Catégorie : AI Workflows
Catégorie : Generative AI

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

mars 2026

Évaluations

10 devoirs

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Il y a 10 modules dans ce cours

This course introduces the powerful concept of Retrieval-Augmented Generation (RAG), a technique used to optimize the performance, accuracy, and cost of generative AI systems. Focused on building AI pipelines with LlamaIndex, Deep Lake, and Pinecone, this course will equip you with the skills to create robust AI models capable of handling complex datasets and delivering traceable, context-aware outputs.

You will explore how to scale RAG pipelines, implement strategies to minimize hallucinations, and improve response accuracy across multimodal AI systems. By the end of the course, you will have hands-on experience optimizing these systems for real-world applications, empowering you to enhance decision-making and operational efficiency. What sets this course apart is its unique combination of theory and practical implementation. By working with cutting-edge tools like LlamaIndex and Pinecone, you'll understand how to balance cost, performance, and accuracy, while gaining insight into the broader context of AI pipelines and decision-making. This course is ideal for data scientists, AI engineers, and MLOps professionals who are looking to expand their expertise in RAG and generative AI. A basic understanding of machine learning concepts is recommended, as the course builds on these foundations to explore more advanced techniques.

In this section, we explore Retrieval Augmented Generation (RAG) frameworks, focusing on naive, advanced, and modular configurations. We implement Python-based RAG systems for improved AI accuracy and adaptability.

Inclus

2 vidéos6 lectures1 devoir

In this section, we cover building and managing RAG pipelines with Deep Lake and OpenAI for efficient AI data handling.

Inclus

1 vidéo4 lectures1 devoir

In this section, we explore index-based RAG pipelines using LlamaIndex, Deep Lake, and OpenAI to enhance traceability, precision, and control in AI-driven data retrieval and generation.

Inclus

1 vidéo5 lectures1 devoir

1 vidéoTotal 1 minute

Building Index-Based RAG with LlamaIndex, Deep Lake, and OpenAI - Overview Video1 minute

5 lecturesTotal 90 minutes

Introduction10 minutes
Building a Semantic Search Engine and Generative Agent for Drone Technology20 minutes
Creating and Populating a Deep Lake Vector Store10 minutes
Vector Store Index Query Engine20 minutes
Tree Index Query Engine30 minutes

1 devoirTotal 10 minutes

Indexing and Retrieval in AI Systems10 minutes

In this section, we explore multimodal modular RAG for drone technology, integrating text and image data retrieval, generation, and performance evaluation using LLMs and MMLLMs.

Inclus

1 vidéo5 lectures1 devoir

1 vidéoTotal 1 minute

Multimodal Modular RAG for Drone Technology - Overview Video1 minute

5 lecturesTotal 70 minutes

Introduction10 minutes
Building a Multimodal Modular RAG Program for Drone Technology10 minutes
Loading and Visualizing the Multimodal Dataset20 minutes
Building a Multimodal Query Engine10 minutes
Multimodal Modular Summary20 minutes

1 devoirTotal 10 minutes

Multimodal RAG in Drone Applications10 minutes

In this section, we explore adaptive RAG with human feedback loops, focusing on improving retrieval quality and integrating expert input.

Inclus

1 vidéo4 lectures1 devoir

In this section, we explore scalable RAG techniques for bank customer data using Pinecone and OpenAI. Key concepts include EDA, vector scaling, and AI-driven recommendations to reduce churn.

Inclus

1 vidéo7 lectures1 devoir

1 vidéoTotal 1 minute

Scaling RAG Bank Customer Data with Pinecone - Overview Video1 minute

7 lecturesTotal 120 minutes

Introduction10 minutes
Collecting the Dataset10 minutes
Data Preparation and Clustering10 minutes
Scaling a Pinecone Index (Vector Store)10 minutes
Chunking30 minutes
Upserting20 minutes
RAG Generative AI30 minutes

1 devoirTotal 10 minutes

Scaling RAG Bank Customer Data with Pinecone10 minutes

In this section, we explore building scalable RAG systems using knowledge graphs, implementing the Wikipedia API, populating a Deep Lake vector store, and constructing a LlamaIndex knowledge graph for semantic search.

Inclus

1 vidéo5 lectures1 devoir

1 vidéoTotal 1 minute

Building Scalable Knowledge-Graph-Based RAG with Wikipedia API and LlamaIndex - Overview Video1 minute

5 lecturesTotal 80 minutes

Introduction10 minutes
Building Graphs from Trees20 minutes
Preparing the Data for Upsertion30 minutes
Re-ranking10 minutes
Metric Calculation and Display10 minutes

1 devoirTotal 10 minutes

Exploring Knowledge-Graph-Based RAG Systems10 minutes

In this section, we explore dynamic RAG using Chroma and Llama, focusing on embedding and querying temporary data for real-time decision-making with open-source tools.

Inclus

1 vidéo5 lectures1 devoir

In this section, we explore RAG data reduction through fine-tuning, focusing on preparing JSONL datasets and evaluating model performance with OpenAI metrics for improved accuracy and cost-effectiveness.

Inclus

1 vidéo3 lectures1 devoir

In this section, we explore RAG pipeline implementation for video generation, embedding video comments in Pinecone, and enhancing labels with GPT-4o analysis for efficient video stock production.

Inclus

1 vidéo7 lectures1 devoir

1 vidéoTotal 1 minute

RAG for Video Stock Production with Pinecone and OpenAI - Overview Video1 minute

7 lecturesTotal 130 minutes

Introduction10 minutes
The Environment of the Video Production Ecosystem10 minutes
Pipeline Generator and Commentator10 minutes
Video Download and Display Functions30 minutes
The Generator and the Commentator10 minutes
Pipeline Controller30 minutes
The Video Expert30 minutes

1 devoirTotal 10 minutes

RAG and AI in Video Production Fundamentals10 minutes

Instructeur

Packt - Course Instructors

Packt

1 836 Cours513 527 apprenants

Offert par

Packt

Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?

Felipe M.

Étudiant(e) depuis 2018

’Pouvoir suivre des cours à mon rythme à été une expérience extraordinaire. Je peux apprendre chaque fois que mon emploi du temps me le permet et en fonction de mon humeur.’

Jennifer J.

Étudiant(e) depuis 2020

’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’

Larry W.

Étudiant(e) depuis 2021

’Lorsque j'ai besoin de cours sur des sujets que mon université ne propose pas, Coursera est l'un des meilleurs endroits où se rendre.’

Chaitanya A.

’Apprendre, ce n'est pas seulement s'améliorer dans son travail : c'est bien plus que cela. Coursera me permet d'apprendre sans limites.’

Foire Aux Questions

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.