🟦 Advanced RAG & AI Engineering Systems Course (LangChain, LlamaIndex, Llama 2)

This advanced AI engineering course focuses on building Retrieval-Augmented Generation (RAG) systems using modern frameworks such as LangChain, LlamaIndex, Chainlit, FastAPI, and open-source large language models like Llama 2. The course is designed to take learners beyond basic LLM usage into production-level AI system design, where intelligence is combined with scalable architecture and real-world deployment practices.

The main idea of the course is to teach how to connect large language models with external knowledge sources in order to improve accuracy, reduce hallucinations, and enable more reliable and context-aware responses. Learners progress from understanding RAG fundamentals to building full production-grade AI systems capable of serving real users at scale.

Throughout the course, emphasis is placed on building practical systems, not just theoretical knowledge. Learners work with real frameworks, APIs, and deployment tools to understand how modern AI applications are engineered in real industry environments.

🟨 1. Introduction to RAG Architecture

This section introduces the core concept of Retrieval-Augmented Generation (RAG) and explains how it has become a key architecture in modern AI systems. It focuses on how combining retrieval mechanisms with large language models allows AI systems to access external knowledge sources dynamically instead of relying only on pre-trained data.

Learners also gain a deeper understanding of how information flows inside RAG systems, starting from user queries, moving through retrieval pipelines, and finally reaching the language model for response generation. This section builds the conceptual foundation required for more advanced implementation topics later in the course.

🟩 1.1 What is RAG Architecture?

This part explains the basic structure of RAG systems, where external documents are retrieved and injected into the model context. Learners understand why this approach is important for improving factual accuracy and reducing hallucination in AI responses.

🟩 1.2 Why RAG Improves AI Systems

Here learners explore the limitations of standalone LLMs and how retrieval-based augmentation helps overcome these issues by grounding responses in real external knowledge sources.

🟨 2. Building First RAG Applications (LangChain, Chainlit, Hugging Face)

This section focuses on hands-on implementation of RAG systems using modern development tools. Learners build their first end-to-end AI application that connects document retrieval with large language model responses.

The section emphasizes practical coding experience, where learners understand how different components such as embeddings, vector stores, and retrieval chains work together to form a complete system.

🟩 2.1 RAG System Pipeline Setup

This part explains how to build the basic pipeline of a RAG system, including loading data, converting it into embeddings, storing it in vector databases, and retrieving relevant context during user queries.

🟩 2.2 Interactive AI Applications with Chainlit

Learners build chat-based AI interfaces using Chainlit, allowing users to interact with RAG systems in real time and receive context-aware responses.

🟨 3. LangChain Agents and Autonomous AI Workflows

This section introduces LangChain agents, which represent a major step beyond simple LLM pipelines. Instead of following fixed steps, agents can reason, plan, and decide which tools to use based on the input they receive.

Learners explore how agents enable more flexible and intelligent workflows that can handle complex tasks involving multiple steps and external tool interactions.

🟩 3.1 Understanding AI Agents

This part explains the concept of AI agents and how they differ from traditional chains by introducing reasoning, decision-making, and autonomy in execution.

🟩 3.2 Tool-Using and Multi-Step Reasoning Agents

Here learners study how agents can interact with external tools, APIs, and functions to perform dynamic tasks that require multiple steps and intelligent decision-making.

🟨 4. LlamaIndex for Enterprise Retrieval Systems

This section focuses on LlamaIndex as a powerful framework for building structured and scalable retrieval systems. It explains how organizations can manage large volumes of documents and data efficiently using indexing and querying techniques.

Learners also understand how LlamaIndex supports enterprise-level AI applications that require reliable and fast access to knowledge bases.

🟩 4.1 Document Indexing and Query Systems

This part explains how documents are converted into structured indexes that allow fast semantic search and retrieval.

🟩 4.2 Enterprise Data Workflows

Here learners explore how LlamaIndex is used in real business environments to manage large-scale knowledge systems and retrieval pipelines.

🟨 5. Production AI Systems with Llama 2 and FastAPI

This section focuses on deploying AI systems into production environments using open-source LLMs like Llama 2 and backend frameworks such as FastAPI. The goal is to move from experimental prototypes to scalable and reliable AI services.

🟩 5.1 Deploying Llama 2 Models

This part explains how to run and serve Llama 2 models efficiently for real-world applications while maintaining performance and scalability.

🟩 5.2 Building Scalable AI APIs with FastAPI

Learners understand how to expose AI systems as APIs, making them accessible for integration into web applications and enterprise platforms.

🟨 6. LLMOps and Observability

This section introduces operational practices for managing AI systems in production. It focuses on monitoring, evaluation, debugging, and optimization to ensure system reliability and performance over time.

🟩 6.1 Monitoring and System Evaluation

This part explains how to track AI system behavior, measure performance, and identify issues in real-time production environments.

🟩 6.2 Using WandB and LangSmith

Learners explore professional tools like Weights & Biases and LangSmith for observability, debugging, and improving AI workflows.

🟨 7. Evaluation and Benchmarking of RAG Systems

This section focuses on measuring the quality and performance of RAG systems using structured evaluation frameworks.

🟩 7.1 RAG Evaluation with RAGAS

This part explains how to evaluate retrieval quality, response accuracy, and contextual relevance in RAG systems.

🟩 7.2 Benchmarking LLM Performance

Here learners compare different models and pipelines using standardized evaluation metrics to determine system effectiveness.

🟨 8. Final Learning Outcomes

By the end of this course, learners will have a strong and practical understanding of Retrieval-Augmented Generation systems, LangChain agents, LlamaIndex workflows, and production AI deployment using FastAPI and Llama 2. They will be able to design and build scalable AI applications that combine intelligent reasoning with external knowledge sources.

In addition, learners will gain experience in monitoring, evaluating, and optimizing AI systems using modern LLMOps tools, enabling them to build enterprise-ready AI solutions capable of handling real-world workloads effectively.