RAG Systems Interview Path

Master RAG (Retrieval-Augmented Generation) interviews with real-world use cases using open source tools. Each scenario includes key topics, interview questions, and technical concepts you'll encounter at top tech companies.

Use Cases

50+

Interview Questions

Building a Document Q&A System with LangChain

IntermediateRAG Fundamentals

Create a production-ready document question-answering system using LangChain and open source LLMs.

🎯 Key Topics to Master:

✓LangChain Document Loaders & Text Splitters

✓Embedding Generation (Sentence-BERT, all-MiniLM)

✓Vector Store Integration (ChromaDB, FAISS)

✓Prompt Engineering for RAG

✓Context Window Management

✓Response Synthesis Strategies

💡 Common Interview Questions:

1.How do you choose the right chunk size for documents?
2.What are the tradeoffs between different embedding models?
3.How do you handle multi-modal documents (PDFs with images)?
4.What strategies improve retrieval accuracy?
5.How do you evaluate RAG system quality?

🔧 Technical Concepts:

RecursiveCharacterTextSplitterHuggingFace embeddingsSimilarity search with metadata filteringRetrievalQA chainSource attribution and citations

🦙

Advanced Retrieval with LlamaIndex

AdvancedAdvanced RAG

Implement sophisticated retrieval strategies using LlamaIndex for complex query scenarios.

🎯 Key Topics to Master:

✓Query Engines & Retrieval Modes

✓Hierarchical Document Indexing

✓Tree Index & Vector Store Index

✓Query Transformations

✓Multi-Document Agents

✓Response Evaluation & Refinement

💡 Common Interview Questions:

1.What are the differences between LangChain and LlamaIndex?
2.How do you implement hierarchical retrieval?
3.What is query routing and when is it useful?
4.How do you handle contradictory information from different sources?
5.What techniques improve response quality?

🔧 Technical Concepts:

VectorStoreIndex and ListIndexQuery engines (retriever, router, sub-question)Response synthesis modesIndex composabilityStreaming responses

🗄️

Vector Database Selection and Optimization

AdvancedVector Databases

Choose and optimize vector databases for production RAG systems at scale.

🎯 Key Topics to Master:

✓ChromaDB for Local Development

✓Weaviate for Production Scale

✓Qdrant for Hybrid Search

✓FAISS for In-Memory Search

✓Index Types (HNSW, IVF, Flat)

✓Performance Tuning & Benchmarking

💡 Common Interview Questions:

1.What are the tradeoffs between different vector databases?
2.How does HNSW indexing work?
3.When should you use approximate vs exact search?
4.How do you handle billions of vectors?
5.What is hybrid search and when is it beneficial?

🔧 Technical Concepts:

Cosine vs Euclidean distanceIndex building parameters (ef_construction, M)Query-time parameters (ef, nprobe)Metadata filtering strategiesBatch operations and bulk indexing

🔍

Embedding Models and Semantic Search

IntermediateEmbeddings

Implement and optimize embedding models for semantic search in RAG applications.

🎯 Key Topics to Master:

✓Open Source Embedding Models

✓Sentence-BERT and BGE Models

✓Cross-Encoder Re-ranking

✓Domain-Specific Fine-tuning

✓Multilingual Embeddings

✓Embedding Dimensionality Tradeoffs

💡 Common Interview Questions:

1.What makes a good embedding model for RAG?
2.How do bi-encoders differ from cross-encoders?
3.When should you fine-tune embeddings?
4.How do you handle domain-specific vocabulary?
5.What is the impact of embedding dimensions on retrieval?

🔧 Technical Concepts:

all-MiniLM-L6-v2 vs BGE-baseMTEB benchmark and evaluationMatryoshka embeddingsEmbedding caching strategiesLate interaction models (ColBERT)

⚡

Hybrid Search: Dense + Sparse Retrieval

AdvancedRetrieval Optimization

Combine dense vector search with traditional keyword search for improved retrieval accuracy.

🎯 Key Topics to Master:

✓BM25 Keyword Search

✓Reciprocal Rank Fusion (RRF)

✓Score Normalization Techniques

✓Query Analysis & Routing

✓Elasticsearch Integration

✓Performance vs Accuracy Tradeoffs

💡 Common Interview Questions:

1.Why combine dense and sparse retrieval?
2.How does reciprocal rank fusion work?
3.What are the limitations of pure vector search?
4.How do you tune hybrid search weights?
5.When is keyword search better than semantic search?

🔧 Technical Concepts:

BM25 algorithm and parametersScore fusion strategiesQuery expansion techniquesElasticsearch dense_vector fieldMulti-vector retrieval

✂️

Advanced Chunking Strategies

AdvancedDocument Processing

Implement sophisticated document chunking strategies for optimal retrieval performance.

🎯 Key Topics to Master:

✓Semantic Chunking

✓Sliding Window with Overlap

✓Sentence Window Retrieval

✓Parent-Child Document Retrieval

✓Context-Aware Chunking

✓Chunk Size Optimization

💡 Common Interview Questions:

1.What problems does semantic chunking solve?
2.How do you handle code blocks and tables?
3.What is the optimal chunk overlap percentage?
4.How do you preserve context across chunks?
5.What is parent-child retrieval?

🔧 Technical Concepts:

Semantic similarity-based chunkingMarkdown structure-aware splittingAuto-merging retrievalHierarchical node parsingMetadata enrichment

🔄

Query Understanding and Rewriting

AdvancedQuery Optimization

Enhance retrieval quality through advanced query processing and transformation techniques.

🎯 Key Topics to Master:

✓Query Decomposition

✓Hypothetical Document Embeddings (HyDE)

✓Query Expansion Techniques

✓Multi-Query Retrieval

✓Step-Back Prompting

✓Query Classification

💡 Common Interview Questions:

1.What is HyDE and when is it useful?
2.How do you handle complex multi-part queries?
3.What is step-back prompting?
4.How do you route queries to appropriate indexes?
5.What are query expansion techniques?

🔧 Technical Concepts:

LLM-based query rewritingSub-question generationQuery intent classificationMulti-query fusionContextual compression

📊

RAG Evaluation and Monitoring

AdvancedEvaluation

Implement comprehensive evaluation frameworks and monitoring for production RAG systems.

🎯 Key Topics to Master:

✓Retrieval Metrics (MRR, NDCG, Recall@k)

✓Generation Quality (Faithfulness, Relevance)

✓RAGAS Framework

✓Human Evaluation Protocols

✓A/B Testing RAG Systems

✓Performance Monitoring

💡 Common Interview Questions:

1.How do you measure RAG system quality?
2.What is faithfulness vs relevance?
3.How do you create evaluation datasets?
4.What metrics indicate retrieval quality?
5.How do you monitor RAG systems in production?

🔧 Technical Concepts:

Context precision and recallAnswer relevancy scoringHallucination detectionLLM-as-judge evaluationSynthetic dataset generation

🖼️

Building Multi-Modal RAG Systems

AdvancedMulti-Modal

Extend RAG to handle images, tables, and other non-text content using open source tools.

🎯 Key Topics to Master:

✓Vision-Language Models (CLIP, LLaVA)

✓Table Understanding & Extraction

✓Image-to-Text Captioning

✓Multi-Vector Retrieval

✓PDF Parsing (Unstructured, PyMuPDF)

✓Cross-Modal Search

💡 Common Interview Questions:

1.How do you handle tables in RAG systems?
2.What are strategies for indexing images?
3.How do you extract information from complex PDFs?
4.What is multi-vector retrieval?
5.How do you search across text and images?

🔧 Technical Concepts:

CLIP embeddings for imagesUnstructured library for parsingTable extraction and summarizationMulti-modal embedding spacesCross-encoder for multi-modal ranking

🚀

Production RAG with Caching and Optimization

AdvancedProduction

Deploy and optimize RAG systems for production with caching, batching, and performance tuning.

🎯 Key Topics to Master:

✓Embedding Caching Strategies

✓Semantic Caching for Responses

✓Batch Processing Optimization

✓Load Balancing & Scaling

✓Cost Optimization Techniques

✓Latency Reduction Strategies

💡 Common Interview Questions:

1.How do you reduce RAG system latency?
2.What is semantic caching?
3.How do you handle traffic spikes?
4.What are cost optimization strategies?
5.How do you scale vector search?

🔧 Technical Concepts:

Redis for semantic cachingEmbedding batch processingApproximate nearest neighbor searchQuantization for embeddingsEdge deployment strategies

📚 How to Use This Path

1. Study Each Use Case

Go through each scenario systematically. Understand the RAG architecture, retrieval strategies, and optimization techniques.

2. Practice Interview Questions

Prepare answers for each question. Focus on explaining tradeoffs between different approaches and tools.

3. Build RAG Projects

Implement at least 2-3 RAG applications using different tools (LangChain, LlamaIndex, vector DBs). Document your choices.

4. Master Open Source Tools

Gain hands-on experience with vector databases, embedding models, and RAG frameworks. Benchmark different approaches.