RAG Systems

Course Overview

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Free Course

For engineers preparing for AI and LLM engineering interviews where retrieval-augmented generation comes up, from first-round concept questions to system design. You will be able to explain and defend every stage of a RAG system — chunking, embeddings, vector indexes, hybrid search, reranking, evaluation, security and agentic retrieval — with short interview answers backed by worked examples.

Instructor: MantraMindAI

Sections: 12

Course Content

Section 1: RAG Fundamentals & Core Concepts

Prepares you to explain what RAG is, why a model on its own is not enough, and how to choose between RAG, fine-tuning and a long context window.

1. What is Retrieval Augmented Generation (RAG), and why is it critical for LLMs?

2. What are the limitations of a pure LLM system without RAG?

3. How is RAG different from fine-tuning an LLM?

4. When should you prefer RAG over fine-tuning?

5. What types of problems are best solved using RAG?

6. What kinds of data sources can be used in a RAG system?

Section 2: RAG Architecture & Workflow

Prepares you to walk an interviewer through a RAG pipeline end to end, from the offline index to a cited answer, and to say where each kind of failure starts.

1. What are the two main components of a RAG application architecture?

2. How does the indexing process work, and why is it essential?

3. What is the step-by-step workflow of a complete RAG pipeline?

4. What is the specific role of the Retrieval component?

5. What is the goal of integrating retrieval with generation?

Section 3: Document Loading & Data Ingestion

Prepares you to explain how raw files and web pages become clean, well-labelled text, and why ingestion quality decides retrieval quality.

1. What is the primary purpose of DocumentLoaders?

2. What are the roles of RecursiveCharacterTextSplitter?

3. What is the specific role of BeautifulSoup in document ingestion?

4. Why is customized HTML parsing critical for a RAG system?

Section 4: Text Splitting & Chunking Strategies

Prepares you to explain why documents are split, how to choose chunk size and overlap with real token numbers, and how chunking mistakes turn into wrong answers.

1. Why is it necessary to split documents into smaller chunks?

2. Why must long documents be split before embedding?

3. What is chunk overlap and why is it used?

4. How does chunk size impact retrieval accuracy?

5. What trade-offs exist between larger chunks and smaller chunks?

6. How does poor chunking lead to hallucinations?

7. Why is RecursiveCharacterTextSplitter recommended for generic text?

8. How does the add_start_index=True parameter help?

9. What is the output of the split_documents() method?

Section 5: Embeddings & Vector Stores

Prepares you to explain how embeddings capture meaning, how cosine similarity ranks them, how vector indexes such as HNSW and IVF trade accuracy for speed and memory, and how to choose and pin an embedding model.

1. What is the purpose of embedding text and storing it in a vector store?

2. How does the choice of embedding model affect RAG performance?

3. What happens if different embedding models are used for indexing and querying?

4. What are the key components needed to build and query a vector store?

5. What is Cosine Similarity and why is it used in vector searches?

Section 6: Vector Search & Retrieval Techniques

Prepares you to explain and tune the retrieval step: retrievers and k, MMR, BM25 and hybrid search with reciprocal rank fusion, metadata filters, and cross-encoder reranking.

1. What is the specific role of a Retriever in LangChain?

2. How does the VectorStoreRetriever function?

3. What does the k parameter control in vector retrieval?

4. What is the difference between similarity search and MMR (Max Marginal Relevance)?

5. What is hybrid search and why is it useful in RAG systems?

6. How do metadata filters improve retrieval quality?

7. What is reranking and why is it important in advanced RAG pipelines?

8. Why should you inspect the documents returned by the retriever?

Section 7: Vector Store Operations & Debugging

Prepares you to explain how you load a vector store safely, look inside it, and trace a bad answer back to a problem in the index.

1. What are common challenges when working with the Chroma vector store?

2. How do you inspect the contents of a vector store?

3. What is the step-by-step workflow to embed and store documents?

Section 8: Prompting & Context Injection

Prepares you to explain how retrieved chunks are placed in the prompt, why more context is not always better, and how you keep the model's answer tied to its sources.

1. How is retrieved context injected into the prompt?

2. What is context stuffing and why is it dangerous?

3. How do you design prompts to prevent hallucinations in RAG?

4. What happens if retrieved documents contradict each other?

5. How do you limit the LLM to answer only from retrieved context?

Section 9: Evaluation, Monitoring & Observability

Prepares you to explain how you measure a RAG system stage by stage, catch hallucinations, trace a bad answer to the step that caused it, and name the usual failure points.

1. How do you evaluate the quality of a RAG system?

2. What metrics are used to measure retrieval quality?

3. How do you detect hallucinations in a RAG pipeline?

4. Why is configuring LangSmith crucial for complex applications?

5. How does LangSmith help in debugging RAG applications?

6. What are common failure modes in RAG systems?

Section 10: Performance, Cost & Scalability

Prepares you to work out a RAG system's latency and cost per query, choose the right caches, keep a large index fresh, and sketch a production design.

1. How do you reduce latency in a RAG system?

2. How do you optimize embedding costs?

3. What caching strategies are used in RAG pipelines?

4. How do you handle large-scale document updates?

5. How do you design a RAG system for production use?

Section 11: Security, Privacy & Enterprise Readiness

Prepares you to explain how a RAG system keeps private documents private: the attacks that come through retrieved text, access control on every chunk, leak prevention, and what compliance teams will ask for.

1. How does RAG help with private or sensitive data?

2. What are the security risks in a RAG-based system?

3. How do you prevent data leakage in RAG responses?

4. How do access controls work in enterprise RAG systems?

5. How do you ensure compliance when using RAG?

Section 12: Advanced & Agentic RAG

Prepares you to explain when a RAG system should plan, route, retry and retrieve in several steps, how to build that safely with tools and graphs, and how user feedback makes it better over time.

1. What is Agentic RAG?

2. How does RAG work with tools and agents?

3. What is self-reflection or self-correction in RAG systems?

4. How does multi-step retrieval improve answer quality?

5. What is RAG with feedback loops?