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Quiz 5: Custom GPTs, Agents & RAG

Test your understanding of custom AI solutions, agents, and retrieval-augmented generation.

Questions

Question 1 (Remember)

What is a Custom GPT?

  1. A completely new language model
  2. A configured version of ChatGPT with specific instructions and knowledge
  3. An open-source alternative to GPT
  4. A fine-tuned model
Answer

B) A configured version of ChatGPT with specific instructions and knowledge - Custom GPTs are ChatGPT instances configured with custom instructions, uploaded knowledge files, and optional Actions—no coding required.

Question 2 (Remember)

What does RAG stand for?

  1. Rapid AI Generation
  2. Retrieval-Augmented Generation
  3. Recursive Algorithm Gateway
  4. Random Access Generator
Answer

B) Retrieval-Augmented Generation - RAG combines information retrieval from external knowledge bases with text generation, grounding AI responses in verified information.

Question 3 (Understand)

Why is RAG important for reducing hallucinations?

  1. It uses more tokens
  2. It increases model size
  3. It grounds responses in retrieved factual information
  4. It requires no prompting
Answer

C) It grounds responses in retrieved factual information - By retrieving relevant documents and using them as context, RAG ensures responses are based on actual sources rather than potentially incorrect model memory.

Question 4 (Understand)

What is the role of embeddings in a RAG system?

  1. To generate images
  2. To represent text as vectors for semantic similarity search
  3. To encrypt data
  4. To reduce costs
Answer

B) To represent text as vectors for semantic similarity search - Embeddings convert text into numerical vectors that capture semantic meaning, enabling efficient similarity search to find relevant documents.

Question 5 (Apply)

You want to create an AI assistant that answers questions about your company's 500-page employee handbook. What's the best approach?

  1. Copy the entire handbook into every prompt
  2. Train a new LLM from scratch
  3. Build a RAG system that retrieves relevant sections
  4. Ignore the handbook
Answer

C) Build a RAG system that retrieves relevant sections - RAG efficiently handles large knowledge bases by retrieving only relevant chunks for each query, rather than processing the entire document every time.

Question 6 (Apply)

You're building a Custom GPT for legal contract review. Which feature would be most valuable?

  1. DALL-E image generation
  2. Uploaded knowledge files with contract templates and legal guidelines
  3. Web browsing capability
  4. Code interpreter
Answer

B) Uploaded knowledge files with contract templates and legal guidelines - Domain-specific knowledge files provide the Custom GPT with accurate reference material for specialized tasks like legal review.

Question 7 (Analyze)

Compare Custom GPTs with API-based integrations in terms of flexibility and ease of use:

  1. Custom GPTs offer more flexibility
  2. Custom GPTs are easier but APIs offer more flexibility
  3. APIs are easier to use
  4. They have identical capabilities
Answer

B) Custom GPTs are easier but APIs offer more flexibility - Custom GPTs require no coding and are quick to create, but APIs provide more customization, integration options, and control over the user experience.

Question 8 (Analyze)

What distinguishes AI agents from simple chatbots?

  1. Agents use more tokens
  2. Chatbots are always better
  3. Agents can autonomously execute multi-step tasks and use tools
  4. There is no difference
Answer

C) Agents can autonomously execute multi-step tasks and use tools - AI agents perceive their environment, make decisions, and take actions autonomously, including using tools and chaining operations without human intervention.

Question 9 (Evaluate)

An organization is choosing between building a Custom GPT versus a full RAG pipeline. They need to query internal documents but have limited technical resources. What would you recommend?

  1. Start with Custom GPT and uploaded files; migrate to RAG if limitations emerge
  2. Always build the RAG pipeline first
  3. Avoid both approaches
  4. Use neither until they hire developers
Answer

A) Start with Custom GPT and uploaded files; migrate to RAG if limitations emerge - Custom GPTs provide a low-code starting point. If document volume, update frequency, or customization needs exceed Custom GPT capabilities, then invest in RAG infrastructure.

Question 10 (Create)

Design a knowledge management system for a consulting firm with: diverse client projects, frequently updated methodologies, and need for accurate, cited responses.

  1. Single Custom GPT with all documents
  2. ChatGPT without customization
  3. RAG system with vector database, organized knowledge bases by domain, citation tracking
  4. Manual document search
Answer

C) RAG system with vector database, organized knowledge bases by domain, citation tracking - Complex knowledge management requires: vector database for efficient retrieval, organized knowledge structure, and citation capability to verify sources.

Score Interpretation

  • 9-10 correct: Excellent understanding of Custom GPTs and RAG
  • 7-8 correct: Good grasp, review missed concepts
  • 5-6 correct: Fair understanding, revisit chapter sections
  • Below 5: Re-read Chapter 5 before proceeding

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