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Session 0: Introduction to RAG Architecture - Test Solutions

📝 Multiple Choice Test Solutions

Question 1: RAG System Architecture

What are the three main stages of a RAG system?
A) Store, Find, Answer
B) Chunk, Embed, Query
C) Index, Retrieve, Generate ✅
D) Parse, Search, Respond

Explanation: The fundamental RAG architecture consists of three main stages: (1) Index - offline preprocessing where documents are chunked, embedded, and stored in vector databases; (2) Retrieve - real-time similarity search to find relevant context for user queries; (3) Generate - LLM synthesis of retrieved context into coherent responses. This three-stage pipeline forms the backbone of all RAG systems.

Question 2: RAG Evolution Timeline

Which RAG evolution phase introduced self-correcting mechanisms?
A) 2022 - LLM Integration
B) 2025 - Next-Gen RAG
C) 2020 - RAG Foundation
D) 2023 - Adaptive RAG ✅

Explanation: The 2023 Adaptive RAG phase introduced self-correcting mechanisms through Self-RAG and Corrective RAG (CRAG) systems. These systems could evaluate their own outputs, decide when to retrieve additional information, and refine responses through multiple iterations. This marked the transition from static "retrieve-then-generate" to intelligent, self-correcting architectures.

Question 3: HyDE Technology

What is the primary advantage of HyDE (Hypothetical Document Embeddings)?
A) Reduces computational cost
B) Simplifies system architecture
C) Eliminates need for vector databases
D) Improves query-document semantic alignment ✅

Explanation: HyDE (Hypothetical Document Embeddings) bridges the semantic gap between user queries and document content by generating hypothetical documents that would answer the query, then searching with those documents instead of the original query. This dramatically improves retrieval because hypothetical answers exist in the same semantic space as actual documents, leading to better query-document alignment.

Question 4: RAG vs Fine-tuning Decision

When should you choose RAG over fine-tuning?
A) When computational resources are unlimited
B) When source attribution is not needed
C) When the domain knowledge is static
D) When you need frequent knowledge updates ✅

Explanation: RAG is superior when knowledge needs frequent updates because it can incorporate new information without expensive model retraining. Fine-tuning is better for static domain knowledge, but RAG excels when you need to add new documents, update facts, or access real-time information while maintaining source attribution and transparency.

Question 5: Agentic RAG Systems

What is the key benefit of Agentic RAG systems?
A) Simpler system architecture
B) Lower computational requirements
C) Faster retrieval speed
D) Iterative query refinement and self-correction ✅

Explanation: Agentic RAG systems (2024-2025 evolution) employ multiple specialized agents that can iteratively refine queries, self-correct responses, and coordinate complex reasoning workflows. This enables sophisticated planning, multi-hop reasoning, and autonomous knowledge acquisition that far exceeds simple retrieve-and-generate approaches.

Performance Scoring

  • 4-5 Correct: Excellent mastery - You have a solid understanding of RAG architecture fundamentals
  • 3 Correct: Good understanding - Minor concepts to review, overall strong foundation
  • 2 Correct: Adequate grasp - Review evolution timeline and technical components
  • 0-1 Correct: Review recommended - Revisit core concepts before proceeding to implementation

Key Concepts Review

Essential RAG Architecture Concepts:

  1. Three-Stage Pipeline: Index → Retrieve → Generate
  2. Evolution Phases: From simple QA (2017) to Agentic systems (2025)
  3. Core Problems: Chunking, semantic gaps, context optimization
  4. Enhancement Techniques: HyDE, query expansion, intelligent processing
  5. Specialization: Domain-specific adaptations for different use cases

Critical Success Factors:

  • Quality indexing with intelligent chunking
  • Effective retrieval with semantic enhancement
  • Coherent generation with proper context integration
  • Continuous evaluation and optimization

Technology Evolution Timeline:

  • 2017-2019: Early Dense Retrieval (DrQA, ORQA)
  • 2020: RAG Foundation (DPR, RAG Paper, REALM, FiD)
  • 2021-2022: Enhanced Fusion (RAG-Fusion, LLM Integration)
  • 2023: Adaptive Systems (Self-RAG, CRAG)
  • 2024-2025: Graph-Based and Agentic RAG

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