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Session 4: Query Enhancement & Context Augmentation - Test Solutions

📝 Multiple Choice Test

Question 1: HyDE Purpose

What is the primary purpose of HyDE (Hypothetical Document Embeddings)?
A) To generate multiple query variations
B) To bridge the semantic gap between queries and documents ✅
C) To compress document embeddings
D) To speed up retrieval performance

Explanation: HyDE bridges the semantic gap by generating hypothetical documents that would answer the user's query, then using these generated documents for retrieval instead of the original query. This technique addresses the mismatch between how users ask questions (query language) and how documents are written (document language).

Question 2: Query Decomposition Approach

When implementing query decomposition, which approach is most effective for complex questions?
A) Random sentence splitting
B) Breaking questions into answerable sub-questions using LLMs ✅
C) Fixed-length query segments
D) Keyword-based fragmentation

Explanation: LLM-based query decomposition intelligently breaks complex questions into logical, answerable sub-questions that maintain semantic meaning. This approach understands question structure and dependencies, unlike mechanical splitting methods that can destroy meaning.

Question 3: Multi-Query Generation Advantage

What is the key advantage of multi-query generation in RAG systems?
A) Reduced computational cost
B) Faster query processing
C) Comprehensive coverage of different query perspectives ✅
D) Simplified system architecture

Explanation: Multi-query generation creates multiple formulations of the same information need, covering different perspectives, specificity levels, and phrasings. This comprehensive coverage increases the likelihood of retrieving relevant documents that might be missed by a single query formulation.

Question 4: Context Window Optimization

In context window optimization, what factor is most important for maintaining quality?
A) Maximum token count
B) Processing speed
C) Balance between relevance and information density ✅
D) Number of source documents

Explanation: The key is balancing relevance (how well the context addresses the query) with information density (how much useful information per token). Simply maximizing tokens or documents can include irrelevant information, while focusing only on speed can sacrifice quality.

Question 5: Prompt Engineering Technique

Which prompt engineering technique is most effective for improving RAG response quality?
A) Longer prompts with more examples
B) Chain-of-thought reasoning with context integration ✅
C) Simple template-based prompts
D) Keyword-heavy prompts

Explanation: Chain-of-thought reasoning guides the model through logical steps while properly integrating retrieved context. This technique helps the model understand relationships between the query, context, and required reasoning, leading to more accurate and well-structured responses.

Performance Scoring

  • 5/5 Correct: Excellent mastery of query enhancement techniques
  • ⅘ Correct: Strong understanding with minor gaps
  • ⅗ Correct: Good grasp of concepts, review HyDE and context optimization
  • ⅖ Correct: Adequate knowledge, focus on prompt engineering strategies
  • ⅕ or below: Recommend hands-on practice with query enhancement pipelines

Key Enhancement Concepts

HyDE Implementation

  1. Semantic Gap Bridging: Query-document language mismatch resolution
  2. Hypothetical Generation: Creating ideal answer documents for matching
  3. Multi-Strategy HyDE: Different document types for various query types
  4. Quality Assessment: Evaluating hypothetical document effectiveness

Query Enhancement Strategies

  1. Query Expansion: Synonym addition, related term inclusion
  2. Query Decomposition: Complex question breakdown into sub-questions
  3. Multi-Query Generation: Multiple perspective coverage
  4. Contextual Enhancement: Domain-specific and user-context aware expansion

Context Window Optimization

  1. Token Budgeting: Efficient allocation of available context space
  2. Relevance Ranking: Prioritizing most relevant information
  3. Information Density: Maximizing useful information per token
  4. Hierarchical Summarization: Smart compression of lower-priority content

Advanced Prompt Engineering

  1. Template Design: Structured prompts for consistent quality
  2. Chain-of-Thought: Step-by-step reasoning guidance
  3. Context Integration: Seamless blending of retrieved information
  4. Dynamic Adaptation: Context-aware prompt selection and modification

Quality Assessment Methods

  1. Relevance Scoring: LLM-based context quality assessment
  2. Confidence Calibration: Uncertainty quantification in responses
  3. Multi-Dimensional Evaluation: Coverage, accuracy, coherence metrics
  4. Continuous Improvement: Feedback loop for enhancement optimization

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