Build powerful RAG pipelines: Traditional, Advanced, Multimodal & Agentic AI with LangChain,LangGraph and Langsmith
β±οΈ Length: 29.4 total hours
β 4.69/5 rating
π₯ 10,466 students
π August 2025 update
Add-On Information:
Get Instant Notification of New Courses on our Telegram channel.
Noteβ Make sure your ππππ¦π² cart has only this course you're going to enroll it now, Remove all other courses from the ππππ¦π² cart before Enrolling!
-
Course Overview
- This bootcamp offers an immersive, hands-on journey into mastering Retrieval Augmented Generation (RAG) paradigms, transforming theoretical knowledge into practical, production-ready AI solutions.
- You will navigate the entire RAG development lifecycle, from initial data preparation and advanced system design to performance optimization, leveraging the full power of LangChain, LangGraph, and LangSmith.
- Discover how to architect intelligent systems capable of accurate, contextually relevant responses by integrating external knowledge, effectively combating LLM hallucinations.
- Progress from foundational RAG techniques to designing sophisticated multi-agent, autonomous AI systems that collaborate to solve complex problems, mirroring cutting-edge AI innovation.
- Acquire a highly sought-after skillset that positions you at the forefront of AI development for creating next-generation intelligent agents and robust enterprise-grade knowledge systems.
-
Requirements / Prerequisites
- A foundational understanding of Python programming, including basic data structures, functions, and object-oriented concepts, as all practical examples and exercises will be coded in Python.
- Familiarity with core concepts of Artificial Intelligence and Machine Learning, such as what constitutes a model, training, inference, and the general purpose of neural networks.
- Basic exposure to Large Language Models (LLMs), understanding their capabilities, limitations, and the concept of prompt engineering, even at an introductory level.
- A stable internet connection and a development environment set up with Python (version 3.8+ recommended) and a code editor like VS Code or Jupyter notebooks.
- While not strictly mandatory, prior experience with fundamental data science libraries like Pandas or NumPy, or basic database interaction, will be beneficial for handling data preparation tasks.
- An eagerness to experiment, troubleshoot, and engage with complex technical challenges, as building robust AI systems often involves iterative refinement and problem-solving.
-
Skills Covered / Tools Used
- Strategic Data Ingestion & Indexing: Master efficient techniques for sourcing, cleaning, chunking, and vectorizing unstructured data, optimizing it for diverse retrieval strategies.
- Advanced Prompt Orchestration: Design sophisticated prompt chains and templates to effectively guide LLMs in utilizing retrieved context for generating coherent and accurate answers, minimizing model “drift.”
- RAG System Architectural Design: Develop the ability to conceptualize and design scalable RAG architectures, selecting appropriate components and workflows for various application requirements.
- Robust Evaluation & Benchmarking: Acquire skills in quantitatively assessing RAG system performance using various metrics (e.g., faithfulness, answer relevance) and establishing reliable evaluation pipelines.
- Dynamic Agentic Workflow Creation: Utilize LangGraph to build complex, stateful multi-step reasoning agents that dynamically adapt their actions based on intermediate outputs, enabling advanced problem-solving.
- Deep Observability & Optimization with LangSmith: Leverage LangSmith’s advanced features for deep-dive analysis, identifying bottlenecks, visualizing trace flows, and conducting A/B tests for continuous RAG improvement.
- Deployment & Scaling Considerations: Understand practical aspects of transitioning RAG applications from development to production, including containerization and managing cloud resources.
- Problem-Solving for RAG Challenges: Develop a systematic approach to diagnose and resolve common RAG issues such as irrelevant context retrieval and poor answer synthesis in real-world scenarios.
-
Benefits / Outcomes
- Emerge as a highly skilled RAG Engineer, capable of designing, building, deploying, and optimizing sophisticated AI applications leveraging external knowledge.
- Gain the confidence to tackle complex real-world challenges across diverse industries, from customer support to advanced research tools.
- Significantly enhance your career prospects in AI, opening doors to highly sought-after roles like AI/ML Engineer, RAG Specialist, or AI Solutions Architect.
- Develop a robust portfolio of RAG projects showcasing your ability to implement traditional, advanced, multimodal, and agentic AI systems.
- Cultivate a deep, practical understanding of the LangChain, LangGraph, and LangSmith ecosystem, enabling you to innovate and adapt to future advancements.
- Master debugging and optimizing complex AI workflows, transforming theoretical knowledge into measurable performance improvements.
-
PROS
- Comprehensive Coverage: Spans the entire spectrum of RAG, from fundamental concepts to cutting-edge agentic architectures.
- Industry-Relevant Tools: Focuses on LangChain, LangGraph, and LangSmith, which are indispensable tools in modern AI development.
- Practical & Project-Oriented: Emphasizes hands-on implementation, allowing students to build real-world, deployable projects.
- Expert-Level Content: Designed to elevate learners to a high proficiency level, suitable for tackling advanced AI engineering challenges.
- Up-to-Date Curriculum: Regularly updated content (August 2025) ensures relevance with the fast-paced AI landscape.
- Strong Community Validation: Highly rated by over 10,000 students, indicating a proven track record of student success and satisfaction.
- Career Acceleration: Provides highly sought-after skills that directly translate into significant career opportunities in AI.
-
CONS
- Significant Time Commitment: Requires dedicated effort over 29.4 hours, which may be challenging for individuals with limited availability.
Learning Tracks: English,Development,Data Science
Found It Free? Share It Fast!