Retrieval-Augmented Generation (RAG) Framework in LLMs - Interview Questions and Answers
In this post, I explain Introduction to RAG in LLMs (Large Language Models), RAG Concepts in LLMs, Retrieval Modules and Vector Embeddings, Indexing Strategies and Vector Databases, Document Ingestion and Preprocessing, RAG in LLM Python, RAG Frameworks (such as LangChain and LlamaIndex), Retrieve‑Then‑Generate vs Generate‑Then‑Retrieve, Prompt Engineering for RAG and Evaluation Metrics for RAG. You can test your knowledge of LLMs in Python by attempting the Quiz after every set of Questions and Answers.
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Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge.
Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge.
Question: What does RAG stand for in the context of LLMs?
Answer: RAG stands for Retrieval-Augmented Generation, a framework that combines an external retrieval component with a generative LLM. Instead of relying solely on model parameters, RAG fetches relevant documents or data snippets and injects them into the prompt, grounding the model’s generative responses in up-to-date information and reducing hallucination.
Answer: RAG stands for Retrieval-Augmented Generation, a framework that combines an external retrieval component with a generative LLM. Instead of relying solely on model parameters, RAG fetches relevant documents or data snippets and injects them into the prompt, grounding the model’s generative responses in up-to-date information and reducing hallucination.
Question: What is the primary reason for using RAG in LLM applications?
Answer: Pure-generation LLMs can hallucinate or produce outdated content because their knowledge is fixed at training time. RAG addresses this by retrieving fresh, domain-specific context at inference, reducing fabrications and providing outputs that reflect the latest or most accurate data sources.
Answer: Pure-generation LLMs can hallucinate or produce outdated content because their knowledge is fixed at training time. RAG addresses this by retrieving fresh, domain-specific context at inference, reducing fabrications and providing outputs that reflect the latest or most accurate data sources.
Question: How does RAG augment a pure-generation model’s capabilities?
Answer: RAG inserts retrieved text (such as paragraphs from a knowledge base) directly into the prompt or model context. The LLM then conditions its next-token predictions on both the original query and the retrieved snippets, effectively looking up references by consulting external evidence before generating.
Answer: RAG inserts retrieved text (such as paragraphs from a knowledge base) directly into the prompt or model context. The LLM then conditions its next-token predictions on both the original query and the retrieved snippets, effectively looking up references by consulting external evidence before generating.
Question: What are the components of a basic RAG architecture?
Answer: A basic RAG system comprises three modules: an embedder that converts queries and documents into vectors; a retrieval index (e.g., FAISS or a vector database) that finds top-k similar documents; and the generator, an LLM that consumes the concatenated query + retrieved passages.
Answer: A basic RAG system comprises three modules: an embedder that converts queries and documents into vectors; a retrieval index (e.g., FAISS or a vector database) that finds top-k similar documents; and the generator, an LLM that consumes the concatenated query + retrieved passages.
Question: In what use cases is RAG especially beneficial?
Answer: RAG is beneficial in knowledge-intensive tasks (such as customer support, scientific Q&A or any research) where users require precise facts. It also enables dynamic domain updates without re-training the entire LLM, making it ideal for rapidly evolving information ecosystems.
Answer: RAG is beneficial in knowledge-intensive tasks (such as customer support, scientific Q&A or any research) where users require precise facts. It also enables dynamic domain updates without re-training the entire LLM, making it ideal for rapidly evolving information ecosystems.
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Quiz
1. Retrieval-Augmented Generation adds which capability to LLMs?
A. Faster training
B. Grounding outputs in external data (Correct)
C. Reducing model parameters
D. Automated fine-tuning
2. The retrieval step in RAG typically uses:
A. Rule-based parsers
B. Vector similarity search (Correct)
C. Decision trees
D. Image embeddings
3. RAG reduces hallucinations by:
A. Increasing generation temperature
B. Incorporating retrieved factual context (Correct)
C. Masking tokens
D. Using larger models
4. A pure-generation model without RAG can only access knowledge:
A. From external APIs
B. Stored in its parameters (weights and biases) at training time (Correct)
C. In real-time databases
D. Via web scraping
5. The advantage of RAG is the ability to:
A. Train the LLM faster
B. Update knowledge without full model retraining (Correct)
C. Eliminate the need for tokenization
D. Guarantee zero inference latency
1. Retrieval-Augmented Generation adds which capability to LLMs?
A. Faster training
B. Grounding outputs in external data (Correct)
C. Reducing model parameters
D. Automated fine-tuning
2. The retrieval step in RAG typically uses:
A. Rule-based parsers
B. Vector similarity search (Correct)
C. Decision trees
D. Image embeddings
3. RAG reduces hallucinations by:
A. Increasing generation temperature
B. Incorporating retrieved factual context (Correct)
C. Masking tokens
D. Using larger models
4. A pure-generation model without RAG can only access knowledge:
A. From external APIs
B. Stored in its parameters (weights and biases) at training time (Correct)
C. In real-time databases
D. Via web scraping
5. The advantage of RAG is the ability to:
A. Train the LLM faster
B. Update knowledge without full model retraining (Correct)
C. Eliminate the need for tokenization
D. Guarantee zero inference latency
Next, understand the RAG flow using the following diagram:
Question: What are the retrieval and generation components in a RAG system?
Answer: The retrieval component converts a user query into a vector and searches an index of document embeddings to return the top-k relevant passages. The generation component then takes those passages and the original query as the prompt to an LLM, which generates a coherent response that is informed by the retrieved context.
Answer: The retrieval component converts a user query into a vector and searches an index of document embeddings to return the top-k relevant passages. The generation component then takes those passages and the original query as the prompt to an LLM, which generates a coherent response that is informed by the retrieved context.
Question: Why is separating retrieval from generation needed in RAG?
Answer: Separating these steps makes sure that the LLM does not rely solely on its frozen training weights; instead, it dynamically consults external evidence. This separation enables scalable indexing of large corpora (for example, view the Testinder software testing & test automation corpora/ dataset on Kaggle here), rapid updates to knowledge bases, and improved factual grounding without retraining the entire model.
Answer: Separating these steps makes sure that the LLM does not rely solely on its frozen training weights; instead, it dynamically consults external evidence. This separation enables scalable indexing of large corpora (for example, view the Testinder software testing & test automation corpora/ dataset on Kaggle here), rapid updates to knowledge bases, and improved factual grounding without retraining the entire model.
Question: How does the high-level architecture of RAG work?
Answer: 1. Query Encoding: The input prompt is encoded into a dense vector.
2. Similarity Search: This vector is matched against a vector database to fetch top-k passages.
3. Context Construction: Retrieved passages are concatenated with the input prompt (query).
4. Generation: The combined context is passed to an LLM, which generates the final response.
This pipeline can be visualized as the following flow: Query → Embedder → Index → Retrieved Docs → Prompt Constructor → LLM → Response.
Answer: 1. Query Encoding: The input prompt is encoded into a dense vector.
2. Similarity Search: This vector is matched against a vector database to fetch top-k passages.
3. Context Construction: Retrieved passages are concatenated with the input prompt (query).
4. Generation: The combined context is passed to an LLM, which generates the final response.
This pipeline can be visualized as the following flow: Query → Embedder → Index → Retrieved Docs → Prompt Constructor → LLM → Response.
Question: What role does the index have in the RAG pipeline?
Answer: The index stores precomputed embeddings of documents or chunks. It enables rapid k-nearest-neighbor searches, typically using libraries like FAISS or specialized vector DBs, returning semantically related passages in milliseconds (a prerequisite for real-time applications).
Answer: The index stores precomputed embeddings of documents or chunks. It enables rapid k-nearest-neighbor searches, typically using libraries like FAISS or specialized vector DBs, returning semantically related passages in milliseconds (a prerequisite for real-time applications).
Question: How does RAG handle updated knowledge without retraining?
Answer: To incorporate new documents, you need to embed them and add them to the index. The retrieval stage then automatically surfaces fresh content in responses. The LLM remains unchanged but gains access to the updated corpus through these index updates.
Answer: To incorporate new documents, you need to embed them and add them to the index. The retrieval stage then automatically surfaces fresh content in responses. The LLM remains unchanged but gains access to the updated corpus through these index updates.
Quiz
1. In RAG, the retrieval step typically returns:
A. Generated tokens
B. Top-k (such as most frequent or highest-ranked) relevant passages (Correct)
C. Model gradients
D. Training data
2. The generation component in RAG uses:
A. A rule-based engine
B. An LLM conditioned on retrieved context (Correct)
C. A decision tree
D. Direct database queries
3. Updating a RAG system’s knowledge base requires:
A. Retraining the LLM
B. Adding new embeddings to the index (Correct)
C. Fine-tuning the full model
D. Changing model hyperparameters
4. The high-level RAG flow diagram begins with:
A. Document embedding
B. Query embedding (Correct)
C. Prompt chaining
D. Response parsing
5. The main benefit of RAG is:
A. Eliminating the need for tokenization
B. Dynamic access to external data without retraining (Correct)
C. Reducing index update frequency
D. Simplifying model architecture
1. In RAG, the retrieval step typically returns:
A. Generated tokens
B. Top-k (such as most frequent or highest-ranked) relevant passages (Correct)
C. Model gradients
D. Training data
2. The generation component in RAG uses:
A. A rule-based engine
B. An LLM conditioned on retrieved context (Correct)
C. A decision tree
D. Direct database queries
3. Updating a RAG system’s knowledge base requires:
A. Retraining the LLM
B. Adding new embeddings to the index (Correct)
C. Fine-tuning the full model
D. Changing model hyperparameters
4. The high-level RAG flow diagram begins with:
A. Document embedding
B. Query embedding (Correct)
C. Prompt chaining
D. Response parsing
5. The main benefit of RAG is:
A. Eliminating the need for tokenization
B. Dynamic access to external data without retraining (Correct)
C. Reducing index update frequency
D. Simplifying model architecture
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Question: What are vector embeddings? Why are they needed for retrieval?
Answer: Vector embeddings are fixed-length numeric representations of text or documents that capture semantic meaning. By mapping words, sentences, or paragraphs into a high-dimensional space, embeddings allow similar content to lie close together, enabling efficient retrieval of related passages using distance metrics rather than exact string matches.
Answer: Vector embeddings are fixed-length numeric representations of text or documents that capture semantic meaning. By mapping words, sentences, or paragraphs into a high-dimensional space, embeddings allow similar content to lie close together, enabling efficient retrieval of related passages using distance metrics rather than exact string matches.
Question: How do you generate embeddings in Python for queries and documents?
Answer: You can use libraries like Sentence-Transformers or OpenAI’s embeddings API. For example with Sentence-Transformers:
Answer: You can use libraries like Sentence-Transformers or OpenAI’s embeddings API. For example with Sentence-Transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")doc_embeddings = model.encode(["Doc A text", "Doc B text"])
query_embedding = model.encode(["User question text"])[0]It produces NumPy arrays you can index and compare.
Question: What is similarity search, and which metrics are commonly used?
Answer: Similarity search finds the nearest neighbors to a query embedding within a set of document embeddings. The common metric is cosine similarity, which measures the angle between vectors; Euclidean distance is another metric. High cosine similarity indicates semantic closeness.
Answer: Similarity search finds the nearest neighbors to a query embedding within a set of document embeddings. The common metric is cosine similarity, which measures the angle between vectors; Euclidean distance is another metric. High cosine similarity indicates semantic closeness.
Question: How do you build an embedding index using FAISS in Python?
Answer: After generating document embeddings, you create a FAISS index and add vectors:
Answer: After generating document embeddings, you create a FAISS index and add vectors:
import faiss
import numpy as np
embeddings = np.stack(doc_embeddings).astype("float32")index = faiss.IndexFlatIP(embeddings.shape[1]) # IP = inner product
index.add(embeddings)This in-memory index supports fast nearest-neighbor queries.
Question: How do you perform a k-nearest-neighbor search on the index?
Answer: Encode the query, then call search on the index. The following returns the top 3 most similar document IDs and their similarity scores.
Answer: Encode the query, then call search on the index. The following returns the top 3 most similar document IDs and their similarity scores.
q_emb = np.array([query_embedding], dtype="float32")
distances, indices = index.search(q_emb, k=3)Note: You can follow Inder P Singh on Kaggle here to get my useful public datasets and code.
Quiz
1. Vector embeddings typically capture which property of text?
A. Exact word order
B. Semantic similarity (Correct)
C. Character frequency
D. File size
2. Which library can you use to generate sentence embeddings locally?
A. NumPy
B. Sentence-Transformers (Correct)
C. Matplotlib
D. Flask
3. Cosine similarity measures:
A. Angle between vectors (Correct)
B. Euclidean distance only
C. Hamming distance
D. Character overlap
4. To retrieve the top 5 documents for a query embedding, you set k to:
A. 1
B. 3
C. 5 (Correct)
D. 10
1. Vector embeddings typically capture which property of text?
A. Exact word order
B. Semantic similarity (Correct)
C. Character frequency
D. File size
2. Which library can you use to generate sentence embeddings locally?
A. NumPy
B. Sentence-Transformers (Correct)
C. Matplotlib
D. Flask
3. Cosine similarity measures:
A. Angle between vectors (Correct)
B. Euclidean distance only
C. Hamming distance
D. Character overlap
4. To retrieve the top 5 documents for a query embedding, you set k to:
A. 1
B. 3
C. 5 (Correct)
D. 10
Question: How does LlamaIndex simplify index creation, compared to raw vector stores?
Answer: LlamaIndex provides the high-level abstraction that automatically handles document ingestion, chunking, embedding, and indexing. It offers connectors to various vector backends and manages metadata, reducing boilerplate code. Developers interact with simple APIs like ServiceContext and Index without orchestrating embedding and storage logic.
Answer: LlamaIndex provides the high-level abstraction that automatically handles document ingestion, chunking, embedding, and indexing. It offers connectors to various vector backends and manages metadata, reducing boilerplate code. Developers interact with simple APIs like ServiceContext and Index without orchestrating embedding and storage logic.
Question: What is the advantage of Pinecone as a managed vector database for RAG?
Answer: Pinecone is a fully managed service that handles index sharding, replication, and real-time updates at scale. It provides global low-latency retrieval, automatic dimensionality management, and built-in metrics. Users can simply insert vectors via API and query using similarity functions without provisioning infrastructure.
Answer: Pinecone is a fully managed service that handles index sharding, replication, and real-time updates at scale. It provides global low-latency retrieval, automatic dimensionality management, and built-in metrics. Users can simply insert vectors via API and query using similarity functions without provisioning infrastructure.
Question: How does Weaviate differentiate itself in schema and hybrid search capabilities?
Answer: Weaviate combines vector search with a GraphQL-based knowledge graph, allowing semantic vectors to be linked via rich schema definitions. It supports hybrid queries (meaning vector similarity plus keyword filters) enabling complex retrieval patterns like "fetch documents about 'machine learning' authored after 2022."
Answer: Weaviate combines vector search with a GraphQL-based knowledge graph, allowing semantic vectors to be linked via rich schema definitions. It supports hybrid queries (meaning vector similarity plus keyword filters) enabling complex retrieval patterns like "fetch documents about 'machine learning' authored after 2022."
Question: What are the benefits of using FAISS for local, on-premise indexing?
Answer: FAISS is an open-source library optimized for high-performance nearest-neighbor search on CPUs and GPUs. It allows multiple index types (flat, IVF, HNSW) and compression options for memory efficiency. FAISS gives developers full control over index parameters, making it useful for research and custom optimizations without external dependencies.
Answer: FAISS is an open-source library optimized for high-performance nearest-neighbor search on CPUs and GPUs. It allows multiple index types (flat, IVF, HNSW) and compression options for memory efficiency. FAISS gives developers full control over index parameters, making it useful for research and custom optimizations without external dependencies.
Question: How do these solutions compare in scalability, cost, and control?
Answer: Managed services like Pinecone and Weaviate scale well but incur ongoing costs, whereas FAISS provides full control and zero service fees at the expense of self-managed infrastructure. LlamaIndex offers rapid prototyping with moderate control. Pinecone excels in global distribution, Weaviate adds semantic filtering, FAISS maximizes customization, and LlamaIndex streamlines integration.
Answer: Managed services like Pinecone and Weaviate scale well but incur ongoing costs, whereas FAISS provides full control and zero service fees at the expense of self-managed infrastructure. LlamaIndex offers rapid prototyping with moderate control. Pinecone excels in global distribution, Weaviate adds semantic filtering, FAISS maximizes customization, and LlamaIndex streamlines integration.
Quiz
1. Which tool offers a GraphQL schema with hybrid vector-and-keyword search?
A. FAISS
B. Pinecone
C. Weaviate (Correct)
D. LlamaIndex
2. The main benefit of FAISS is:
A. Managed sharding
B. Full local control and high performance (Correct)
C. Automatic schema management
D. Global low-latency replication
3. Pinecone offers:
A. Open-source library only
B. Fully managed scaling and metrics (Correct)
C. GraphQL support
D. CLI-only interface
4. LlamaIndex’s main advantage is:
A. Eliminating the need for embeddings
B. Providing a unified API for rapid index creation (Correct)
C. Hosting vector stores itself
D. Built-in LLM training
1. Which tool offers a GraphQL schema with hybrid vector-and-keyword search?
A. FAISS
B. Pinecone
C. Weaviate (Correct)
D. LlamaIndex
2. The main benefit of FAISS is:
A. Managed sharding
B. Full local control and high performance (Correct)
C. Automatic schema management
D. Global low-latency replication
3. Pinecone offers:
A. Open-source library only
B. Fully managed scaling and metrics (Correct)
C. GraphQL support
D. CLI-only interface
4. LlamaIndex’s main advantage is:
A. Eliminating the need for embeddings
B. Providing a unified API for rapid index creation (Correct)
C. Hosting vector stores itself
D. Built-in LLM training
Question: How can you parse diverse document formats for RAG ingestion?
Answer: You can use libraries like PyPDF2 for PDFs, python-docx for Word, and beautifulsoup4 for HTML. After loading raw text, normalize whitespace, remove headers/footers, and unify encoding. Example:
Answer: You can use libraries like PyPDF2 for PDFs, python-docx for Word, and beautifulsoup4 for HTML. After loading raw text, normalize whitespace, remove headers/footers, and unify encoding. Example:
from PyPDF2 import PdfReader
reader = PdfReader("doc.pdf")text = "\n".join(page.extract_text() for page in reader.pages)
# Note: some PDFs/pages may return None for extract_text() if content is scanned images (OCR required).
Question: What is chunking? Why is it important?
Answer: Chunking splits long documents into manageable segments (such as 500-token windows with 50-token overlap) for efficient embedding and retrieval. It balances context size against vector index granularity, so that the retrieved passages remain coherent. Example:
Answer: Chunking splits long documents into manageable segments (such as 500-token windows with 50-token overlap) for efficient embedding and retrieval. It balances context size against vector index granularity, so that the retrieved passages remain coherent. Example:
tokens = tokenizer.encode(text)
chunks = [tokens[i:i+500] for i in range(0, len(tokens), 450)]
Question: How do you add metadata tags to each chunk?
Answer: Attach key-value pairs like
Answer: Attach key-value pairs like
{"source": "doc.pdf", "page": 3, "chunk_id": 2}to each chunk before embedding. This metadata is stored along with embedding, enabling traceable retrieval and post-generation attribution. Question: How does overlap between chunks affect retrieval quality?
Answer: Controlled overlap makes sure that boundary content isn’t lost. It provides context continuity so that key sentences spanning chunk edges remain recoverable, improving answer completeness during retrieval. You can adjust overlap size based on document structure.
Answer: Controlled overlap makes sure that boundary content isn’t lost. It provides context continuity so that key sentences spanning chunk edges remain recoverable, improving answer completeness during retrieval. You can adjust overlap size based on document structure.
Question: What preprocessing steps improve embedding accuracy?
Answer: Lowercase normalization, punctuation removal (or preservation depending on domain), and stop-word filtering can sharpen semantic focus. For specialized corpora, custom tokenization rules (such as keeping domain vocabulary intact) enhance embedding fidelity.
Answer: Lowercase normalization, punctuation removal (or preservation depending on domain), and stop-word filtering can sharpen semantic focus. For specialized corpora, custom tokenization rules (such as keeping domain vocabulary intact) enhance embedding fidelity.
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Quiz
1. Which is a common library used to extract text from PDF files?
A. python-docx
B. beautifulsoup4
C. PyPDF2 (Correct)
D. pandas
2. Chunking with overlap prevents:
A. Model overfitting
B. Loss of context at segment boundaries (Correct)
C. High memory usage
D. Index compression
3. Attaching metadata to chunks allows:
A. Faster tokenization
B. Traceable retrieval and precise attribution (Correct)
C. GPU acceleration
D. Model fine-tuning
4. Over-normalizing text by removing punctuation may:
A. Improve grammatical accuracy
B. Degrade semantic embeddings if punctuation carries meaning (Correct)
C. Increase index size
D. Eliminate need for chunking
5. A practical overlap size when chunking 500 tokens might be:
A. 0 tokens
B. 50 tokens (Correct)
C. 500 tokens
D. 1000 tokens
1. Which is a common library used to extract text from PDF files?
A. python-docx
B. beautifulsoup4
C. PyPDF2 (Correct)
D. pandas
2. Chunking with overlap prevents:
A. Model overfitting
B. Loss of context at segment boundaries (Correct)
C. High memory usage
D. Index compression
3. Attaching metadata to chunks allows:
A. Faster tokenization
B. Traceable retrieval and precise attribution (Correct)
C. GPU acceleration
D. Model fine-tuning
4. Over-normalizing text by removing punctuation may:
A. Improve grammatical accuracy
B. Degrade semantic embeddings if punctuation carries meaning (Correct)
C. Increase index size
D. Eliminate need for chunking
5. A practical overlap size when chunking 500 tokens might be:
A. 0 tokens
B. 50 tokens (Correct)
C. 500 tokens
D. 1000 tokens
Question: How do you retrieve relevant chunks for a user query in Python?
Answer: First, embed the query using your SentenceTransformer or OpenAI embeddings client. Then perform a k-nearest-neighbor search on your vector index. Example:
Answer: First, embed the query using your SentenceTransformer or OpenAI embeddings client. Then perform a k-nearest-neighbor search on your vector index. Example:
query_emb = embed_model.encode([user_query])[0].astype("float32") distances, indices = faiss_index.search(query_emb.reshape(1, -1), k=3)
relevant_chunks = [documents[i] for i in indices[0]]
Question: How do you compose a prompt that injects retrieved context into the LLM input?
Answer: Concatenate the retrieved chunks with clear delimiters, then append the user question. For example:
Answer: Concatenate the retrieved chunks with clear delimiters, then append the user question. For example:
context = "\n\n".join(relevant_chunks)
prompt = (
f"Use the following context to answer the question.\n\n"
f"Context:\n{context}\n\n" f"Question: {user_query}\nAnswer:" )
Question: Which Python client call invokes an LLM for generation on the composed prompt?
Answer: With OpenAI’s Responses API:
Answer: With OpenAI’s Responses API:
# pip install openai
import os
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
prompt = f"Use the following context to answer the question.\n\nContext:\n{context}\n\nQuestion:{user_query}\nAnswer:"
response = client.responses.create(
model="gpt-5",
input=[
{"role": "system", "content": "You are an expert assistant."},
{"role": "user", "content": prompt},
],
temperature=0.2,
max_tokens=200
)
# Helper: get the text output in a robust way
answer = response.output_text # concise helper exposed by the SDK
print(answer)
Question: Can you provide a complete Example combining retrieval and generation?
Answer: Compatible Code Example (Chat Completions style)
Answer: Compatible Code Example (Chat Completions style)
© Inder P Singh https://www.linkedin.com/in/inderpsingh
Quiz
1. Which method retrieves the most semantically similar passages from FAISS?
A. index.search (Correct)
B. index.add
C. model.generate
D. tokenizer.encode
2. When composing a RAG prompt, you should:
A. Only include the user question
B. Concatenate retrieved context before the question (Correct)
C. Send each chunk as a separate API call
D. Mask the context tokens
3. In the OpenAI call, the messages array must include a:
A. System role instruction and user role content (Correct)
B. Assistant role only
C. File upload field
D. Trainer object
4. To reduce hallucinations, you set temperature to a low value such as:
A. 1.0
B. 0.8
C. 0.2 (Correct)
D. 2.0
5. In the full Python example, k=2 specifies retrieving:
A. Two contexts for grounding the LLM (Correct)
B. Two tokens only
C. The entire document
D. Two model calls
1. Which method retrieves the most semantically similar passages from FAISS?
A. index.search (Correct)
B. index.add
C. model.generate
D. tokenizer.encode
2. When composing a RAG prompt, you should:
A. Only include the user question
B. Concatenate retrieved context before the question (Correct)
C. Send each chunk as a separate API call
D. Mask the context tokens
3. In the OpenAI call, the messages array must include a:
A. System role instruction and user role content (Correct)
B. Assistant role only
C. File upload field
D. Trainer object
4. To reduce hallucinations, you set temperature to a low value such as:
A. 1.0
B. 0.8
C. 0.2 (Correct)
D. 2.0
5. In the full Python example, k=2 specifies retrieving:
A. Two contexts for grounding the LLM (Correct)
B. Two tokens only
C. The entire document
D. Two model calls
Question: What is LangChain? How does it support RAG workflow?
Answer: LangChain is a Python framework that orchestrates LLM calls, prompt templates, and retrieval components into end-to-end pipelines. It provides abstractions like RetrievalQA chains, which plug in any vector store and LLM to perform retrieval-conditioned generation. Developers can define a chain by specifying a retriever (e.g., FAISS or Pinecone) and a language model, then call chain.run(question) to get grounded answers.
Answer: LangChain is a Python framework that orchestrates LLM calls, prompt templates, and retrieval components into end-to-end pipelines. It provides abstractions like RetrievalQA chains, which plug in any vector store and LLM to perform retrieval-conditioned generation. Developers can define a chain by specifying a retriever (e.g., FAISS or Pinecone) and a language model, then call chain.run(question) to get grounded answers.
Question: How does LlamaIndex differ from LangChain in building RAG applications?
Answer: LlamaIndex focuses on simplifying data ingestion and index management. It wraps document loaders, chunkers, embedders, and indexes into a unified API. With a single GPTSimpleVectorIndex.from_documents(documents) call, LlamaIndex builds the RAG index and lets developers query with index.query("…"). The framework handles prompt construction internally, optimizing context inclusion.
Answer: LlamaIndex focuses on simplifying data ingestion and index management. It wraps document loaders, chunkers, embedders, and indexes into a unified API. With a single GPTSimpleVectorIndex.from_documents(documents) call, LlamaIndex builds the RAG index and lets developers query with index.query("…"). The framework handles prompt construction internally, optimizing context inclusion.
Question: What is RetrievalQA, and how is it used in these frameworks?
Answer: RetrievalQA is a component pattern that encapsulates retrieval and generation logic. It first retrieves top-k passages for a question, then constructs a prompt that includes those passages and sends it to the LLM. In LangChain you instantiate it with RetrievalQA.from_chain_type(llm, retriever). In LlamaIndex, similar functionality is exposed via the query method on an index object.
Answer: RetrievalQA is a component pattern that encapsulates retrieval and generation logic. It first retrieves top-k passages for a question, then constructs a prompt that includes those passages and sends it to the LLM. In LangChain you instantiate it with RetrievalQA.from_chain_type(llm, retriever). In LlamaIndex, similar functionality is exposed via the query method on an index object.
Question: Can you give an Example of a LangChain RAG pipeline in Python?
Answer:
Answer:
Quiz
1. In LangChain, which class encapsulates retrieval and generation for RAG?
A. VectorIndex
B. RetrievalQA (Correct)
C. PromptTemplate
D. TextSplitter
2. LlamaIndex’s from_documents method handles:
A. Only embedding
B. Document loading, chunking, embedding, and indexing (Correct)
C. Model training
D. API key management
3. The main role of RetrievalQA is to:
A. Train new embeddings
B. Wrap retrieval and LLM calls into a single pipeline (Correct)
C. Evaluate model metrics
D. Visualize index performance
4. In the LangChain example, FAISS.from_documents requires:
A. A list of document texts and an embedding model (Correct)
B. Only an LLM instance
C. A GPU cluster connection
D. A prompt template
5. The advantage of using the frameworks for RAG is:
A. Eliminating the need for tokenization
B. Reducing boilerplate code for RAG pipelines (Correct)
C. Automatically generating embeddings without a model
D. Guaranteeing lower inference latency
1. In LangChain, which class encapsulates retrieval and generation for RAG?
A. VectorIndex
B. RetrievalQA (Correct)
C. PromptTemplate
D. TextSplitter
2. LlamaIndex’s from_documents method handles:
A. Only embedding
B. Document loading, chunking, embedding, and indexing (Correct)
C. Model training
D. API key management
3. The main role of RetrievalQA is to:
A. Train new embeddings
B. Wrap retrieval and LLM calls into a single pipeline (Correct)
C. Evaluate model metrics
D. Visualize index performance
4. In the LangChain example, FAISS.from_documents requires:
A. A list of document texts and an embedding model (Correct)
B. Only an LLM instance
C. A GPU cluster connection
D. A prompt template
5. The advantage of using the frameworks for RAG is:
A. Eliminating the need for tokenization
B. Reducing boilerplate code for RAG pipelines (Correct)
C. Automatically generating embeddings without a model
D. Guaranteeing lower inference latency
Question: What is the Retrieve-Then-Generate architecture in RAG systems?
Answer: In Retrieve-Then-Generate, the pipeline first performs a retrieval step to fetch top-k relevant documents, then generates an answer by conditioning the LLM on those retrieved passages. This enables the LLM to ground its output in external context before any text is produced.
Example: A legal assistant retrieves the relevant clause text from a contract, then asks the LLM to summarize obligations based solely on that clause.
Answer: In Retrieve-Then-Generate, the pipeline first performs a retrieval step to fetch top-k relevant documents, then generates an answer by conditioning the LLM on those retrieved passages. This enables the LLM to ground its output in external context before any text is produced.
Example: A legal assistant retrieves the relevant clause text from a contract, then asks the LLM to summarize obligations based solely on that clause.
Question: What defines the Generate-Then-Retrieve pattern?
Answer: In Generate-Then-Retrieve, the LLM first generates an initial draft or outline from the user query alone, then uses that generated text to perform retrieval, fetching documents that match the draft’s content. Finally, the system refines the draft by integrating the newly retrieved passages. This can capture creative ideas early, then ground them.
Example: A research bot generates bullet points on a topic, then retrieves and cites supporting studies matching those points.
Answer: In Generate-Then-Retrieve, the LLM first generates an initial draft or outline from the user query alone, then uses that generated text to perform retrieval, fetching documents that match the draft’s content. Finally, the system refines the draft by integrating the newly retrieved passages. This can capture creative ideas early, then ground them.
Example: A research bot generates bullet points on a topic, then retrieves and cites supporting studies matching those points.
Question: How do latency characteristics differ between the two patterns?
Answer: Retrieve-Then-Generate typically has predictable latency: one retrieval call followed by one generation call. Generate-Then-Retrieve incurs two model calls (initial generation + refinement), plus retrieval, with possibly more generation latency. However, if the initially generated draft is short, retrieval may execute faster on a smaller query.
Answer: Retrieve-Then-Generate typically has predictable latency: one retrieval call followed by one generation call. Generate-Then-Retrieve incurs two model calls (initial generation + refinement), plus retrieval, with possibly more generation latency. However, if the initially generated draft is short, retrieval may execute faster on a smaller query.
Question: What are the accuracy trade-offs of each approach?
Answer: Retrieve-Then-Generate typically has higher factual accuracy since the LLM never strays from provided evidence.
Generate-Then-Retrieve can produce more creative or exploratory outputs but risks hallucination in the first draft. Grounding in the second stage mitigates this risk, but some errors may persist if not fully overwritten.
Answer: Retrieve-Then-Generate typically has higher factual accuracy since the LLM never strays from provided evidence.
Generate-Then-Retrieve can produce more creative or exploratory outputs but risks hallucination in the first draft. Grounding in the second stage mitigates this risk, but some errors may persist if not fully overwritten.
Question: How does freshness of information affect the architectures following these patterns?
Answer: Both patterns access the same retrieval index, so freshness depends on index updates. However, Generate-Then-Retrieve can inadvertently overweight stale model knowledge in its initial draft, whereas Retrieve-Then-Generate relies entirely on up-to-date retrieval, so that the newest data drives the response.
Answer: Both patterns access the same retrieval index, so freshness depends on index updates. However, Generate-Then-Retrieve can inadvertently overweight stale model knowledge in its initial draft, whereas Retrieve-Then-Generate relies entirely on up-to-date retrieval, so that the newest data drives the response.
Quiz
1. In Retrieve-Then-Generate, the LLM sees retrieved context:
A. Before generating output (Correct)
B. After generating a draft
C. Only during fine-tuning
D. Never
2. Generate-Then-Retrieve requires how many LLM invocations at minimum?
A. One
B. Two (Correct)
C. Three
D. Zero
3. Which pattern is more likely to minimize hallucinations?
A. Retrieve-Then-Generate (Correct)
B. Generate-Then-Retrieve
C. Both equally
D. Neither
4. If you need the freshest data to drive your LLM answer, you should choose:
A. Generate-Then-Retrieve
B. Retrieve-Then-Generate (Correct)
C. Either, since both use the same index
D. Neither
5. A disadvantage of Generate-Then-Retrieve is:
A. Higher retrieval latency only
B. Potential persistence of initial hallucinations after retrieval (Correct)
C. No use of external context
D. Inability to update the index dynamically
1. In Retrieve-Then-Generate, the LLM sees retrieved context:
A. Before generating output (Correct)
B. After generating a draft
C. Only during fine-tuning
D. Never
2. Generate-Then-Retrieve requires how many LLM invocations at minimum?
A. One
B. Two (Correct)
C. Three
D. Zero
3. Which pattern is more likely to minimize hallucinations?
A. Retrieve-Then-Generate (Correct)
B. Generate-Then-Retrieve
C. Both equally
D. Neither
4. If you need the freshest data to drive your LLM answer, you should choose:
A. Generate-Then-Retrieve
B. Retrieve-Then-Generate (Correct)
C. Either, since both use the same index
D. Neither
5. A disadvantage of Generate-Then-Retrieve is:
A. Higher retrieval latency only
B. Potential persistence of initial hallucinations after retrieval (Correct)
C. No use of external context
D. Inability to update the index dynamically
Question: How should you structure a RAG prompt template to incorporate retrieved documents?
Answer: Begin with a system instruction clarifying the role, then include a Context section listing retrieved passages with clear delimiters, followed by the user’s question. This hierarchy makes the LLM prioritize external evidence before generating.
Example:
Answer: Begin with a system instruction clarifying the role, then include a Context section listing retrieved passages with clear delimiters, followed by the user’s question. This hierarchy makes the LLM prioritize external evidence before generating.
Example:
Question: How can you avoid hallucinations when writing RAG prompts?
Answer: Add explicit instructions to ground answers in the provided context and to respond "I don't know" if information is missing. This forces the model to defer rather than guess.
Example: Answer only based on the Context above. If the answer is not present, respond "I don't know."
Answer: Add explicit instructions to ground answers in the provided context and to respond "I don't know" if information is missing. This forces the model to defer rather than guess.
Example: Answer only based on the Context above. If the answer is not present, respond "I don't know."
Question: What role do few-shot examples have in RAG prompt templates?
Answer: Embedding one or two illustrative question-answer pairs showing how to use context guides the model’s formatting and reasoning. You can place these examples before the live query to set clear patterns.
Example:
Answer: Embedding one or two illustrative question-answer pairs showing how to use context guides the model’s formatting and reasoning. You can place these examples before the live query to set clear patterns.
Example:
Q: What color is the sky?
A: The sky is blue.
Now, answer: Question: {user_question} Question: How can you ensure conciseness in RAG prompts to fit context windows?
Answer: Use numbered lists or bullet markers in the Context section and limit each passage to essential sentences. Truncate or summarize long chunks before inclusion.
Example:
Context:
1. Revenue increased 10%.
2. Costs reduced by 5%.
Answer: Use numbered lists or bullet markers in the Context section and limit each passage to essential sentences. Truncate or summarize long chunks before inclusion.
Example:
Context:
1. Revenue increased 10%.
2. Costs reduced by 5%.
Quiz
1. A well-structured RAG prompt begins with:
A. The user question
B. A system role instruction (Correct)
C. Retrieved passages only
D. Model parameters
2. To prevent guessing, prompts should include:
A. "Use external APIs"
B. "Answer based solely on the Context" (Correct)
C. "Generate additional context"
D. "Train on new data"
3. Few-shot examples in RAG prompts help by:
A. Increasing token count
B. Showing proper use of context and answer format (Correct)
C. Eliminating the need for retrieval
D. Masking irrelevant tokens
4. For fitting within context windows, you should:
A. Include entire documents
B. Summarize or truncate context passages (Correct)
C. Omit delimiters
D. Use images instead
5. A delimiter like "---" between passages serves to:
A. Compress embeddings
B. Visually separate context chunks for clarity (Correct)
C. Increase hallucinations
D. Bypass retrieval
1. A well-structured RAG prompt begins with:
A. The user question
B. A system role instruction (Correct)
C. Retrieved passages only
D. Model parameters
2. To prevent guessing, prompts should include:
A. "Use external APIs"
B. "Answer based solely on the Context" (Correct)
C. "Generate additional context"
D. "Train on new data"
3. Few-shot examples in RAG prompts help by:
A. Increasing token count
B. Showing proper use of context and answer format (Correct)
C. Eliminating the need for retrieval
D. Masking irrelevant tokens
4. For fitting within context windows, you should:
A. Include entire documents
B. Summarize or truncate context passages (Correct)
C. Omit delimiters
D. Use images instead
5. A delimiter like "---" between passages serves to:
A. Compress embeddings
B. Visually separate context chunks for clarity (Correct)
C. Increase hallucinations
D. Bypass retrieval
Question: What is R-precision? How does it measure relevance in RAG?
Answer: R-precision computes the fraction of relevant documents retrieved among the top-R results, where R equals the total number of relevant documents for a query. In RAG, if there are 10 gold-standard passages and the system returns those 10 within its top-10 retrievals, R-precision is 1.0.
Answer: R-precision computes the fraction of relevant documents retrieved among the top-R results, where R equals the total number of relevant documents for a query. In RAG, if there are 10 gold-standard passages and the system returns those 10 within its top-10 retrievals, R-precision is 1.0.
Question: How is factuality assessed in generated answers?
Answer: Factuality compares each statement in the LLM’s response against a trusted source (the retrieved passages). You can use automated fact-checking tools or human annotations to score the proportion of claims that are verifiably present in the context.
Answer: Factuality compares each statement in the LLM’s response against a trusted source (the retrieved passages). You can use automated fact-checking tools or human annotations to score the proportion of claims that are verifiably present in the context.
Question: What does end-to-end accuracy indicate in a RAG pipeline?
Answer: End-to-end accuracy measures whether the final answer correctly addresses the user’s query using the combined retrieval + generation process. It’s the percentage of questions for which the RAG system’s output matches a reference answer, reflecting both retrieval quality and generation correctness.
Answer: End-to-end accuracy measures whether the final answer correctly addresses the user’s query using the combined retrieval + generation process. It’s the percentage of questions for which the RAG system’s output matches a reference answer, reflecting both retrieval quality and generation correctness.
Question: How can mean reciprocal rank (MRR) supplement R-precision evaluations?
Answer: MRR averages the reciprocal rank of the first relevant document across queries. If the first correct passage appears at position 2, its reciprocal rank is 0.5. MRR rewards systems that place relevant documents earlier in the retrieval list, improving generator grounding.
Answer: MRR averages the reciprocal rank of the first relevant document across queries. If the first correct passage appears at position 2, its reciprocal rank is 0.5. MRR rewards systems that place relevant documents earlier in the retrieval list, improving generator grounding.
Question: Why is human evaluation important for RAG metrics?
Answer: Automated metrics may not capture nuanced correctness, coherence, or writing quality. Human users rate responses on fluency, completeness, and adherence to context, providing qualitative insights that numbers alone cannot provide.
Answer: Automated metrics may not capture nuanced correctness, coherence, or writing quality. Human users rate responses on fluency, completeness, and adherence to context, providing qualitative insights that numbers alone cannot provide.
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Quiz
1. R-precision of 0.8 indicates that:
A. 80% of retrieved documents are relevant within the top-R (Correct)
B. 80% of all relevant documents were retrieved at any rank
C. The first relevant document is at rank 0.8
D. The retrieval time is 0.8 seconds
2. Factuality scoring in RAG relies on:
A. Comparing generated claims against retrieved context (Correct)
B. Model perplexity scores
C. Token count differences
D. Index update frequency
3. End-to-end accuracy combines evaluation of:
A. Retrieval only
B. Generation only
C. Both retrieval and generation correctness (Correct)
D. Embedding dimensionality
4. MRR emphasizes:
A. Quantity of documents retrieved
B. Position of the first relevant document (Correct)
C. Overall query throughput
D. Factual consistency
5. Automated metrics may miss:
A. Document vectorization errors
B. Human-perceived coherence and completeness (Correct)
C. Retrieval latency
D. Tokenization accuracy
1. R-precision of 0.8 indicates that:
A. 80% of retrieved documents are relevant within the top-R (Correct)
B. 80% of all relevant documents were retrieved at any rank
C. The first relevant document is at rank 0.8
D. The retrieval time is 0.8 seconds
2. Factuality scoring in RAG relies on:
A. Comparing generated claims against retrieved context (Correct)
B. Model perplexity scores
C. Token count differences
D. Index update frequency
3. End-to-end accuracy combines evaluation of:
A. Retrieval only
B. Generation only
C. Both retrieval and generation correctness (Correct)
D. Embedding dimensionality
4. MRR emphasizes:
A. Quantity of documents retrieved
B. Position of the first relevant document (Correct)
C. Overall query throughput
D. Factual consistency
5. Automated metrics may miss:
A. Document vectorization errors
B. Human-perceived coherence and completeness (Correct)
C. Retrieval latency
D. Tokenization accuracy
Want to learn more? If you want my complete Retrieval-Augmented Generation (RAG) Framework in LLMs document that additionally includes the following important topics, you can message me on LinkedIn:
Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge.
Optimization and Caching, Advanced RAG Techniques (such as RAG multimodal retrieval), RAG in LLamaIndex Example with code, Best Practices and Troubleshooting RAG and RAG in LLM consolidated Quiz with multiple‑choice questions and answers to test your knowledge.
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