RAG_Master_Deck_AI_Engineer, Course For AI Engineers

RAG_Master_Deck_AI_Engineer, Course For AI Engineers

• 1.
  RAG – RetrievalAugmented Generation Complete Interview & Production Guide For AI Engineer Roles
• 2.
  What is RAG? RAGcombines LLMs with external knowledge retrieval to generate accurate, grounded answers.
• 3.
  Why RAG? Problem Whyit happens Hallucination Model guesses when it lacks knowledge Stale knowledge Training data cutoff No access to private data Internal company data missing High fine-tuning cost Training is expensive Poor explainability No sources
• 4.
  Why RAG? Problem RAGSolution Hallucination Grounding with retrieved facts Knowledge cutoff Live data access Private data Secure retrieval Cost No fine-tuning needed Explainability Source citations
• 5.
  RAG Architecture User →Embed → Vector Search → Retrieve Docs → Prompt → LLM → Answer
• 6.
  Core Components Loader Chunker Embedding Model VectorDB Retriever LLM
• 7.
  Document Pipeline Load →Clean → Chunk → Embed → Store
• 8.
  Chunking Strategy Small chunks→ High recall Large chunks → High precision Typical: 512 tokens
• 9.
  Embedding Models OpenAI text-embedding BGE Instructor E5
• 10.
  Vector Databases FAISS Pinecone Weaviate Milvus Qdrant
• 11.
  Retrieval Types Dense Sparse Hybrid (best)
• 12.
  Re-ranking Cross encoder improvesaccuracy and reduces hallucinations
• 13.
  Prompt Template Context injection Strictgrounding instructions
• 14.
  End-to-End Flow Query →Embed → Search → Retrieve → Re-rank → Prompt → LLM
• 15.
  RAG vs Fine-Tuning RAG:dynamic & cheap Fine-tune: static & costly
• 16.
  Advanced RAG Multi-hop Hierarchical Graph RAG AgenticRAG
• 17.
  Hallucination Control Hybrid search Re-ranking Citations Answerverification
• 18.
  Evaluation Recall@K Precision@K Faithfulness RAGAS
• 19.
  Scaling Architecture API →Cache → Retriever → Vector DB → LLM
• 20.
  Cost Optimization Caching Quantization Prompt compression Routing
• 21.
  Security Prompt injection Data leakage Outputfiltering
• 22.
  Interview Questions Why RAG? Chunksize? Scaling strategy? Hybrid search?
• 23.
  Production Stack LLM Framework Vector DB API GPUinfra
• 24.
  Final Rule Dynamic knowledge→ RAG Behavior change → Fine-tuning