Questions
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1What is RAG and why is it preferred over fine-tuning for domain-specific knowledge in production applications?
2What are the core components of a RAG pipeline in LangChain — Document Loaders, Text Splitters, Embeddings, Vector Stores, Retrievers, and Chains?
3What is the difference between semantic search and keyword search and why does RAG rely on semantic similarity?
4What is an Embedding in the context of RAG — what does it represent and why is cosine similarity used to compare them?
5What is a Vector Store and how does it differ from a traditional relational or document database?
6What is the difference between a Retriever and a Vector Store in LangChain — why is the abstraction separation important?
7What is a Document object in LangChain — what are pageContent and metadata fields and why does metadata matter in RAG?
8What are Document Loaders in LangChain and how do you choose the right loader for PDFs, web pages, Notion, Google Drive, or SQL databases?
9What is the difference between RecursiveCharacterTextSplitter and CharacterTextSplitter — when would you use one over the other?
10What is chunk size and chunk overlap in text splitting — how do you decide the right values for your use case?
11How do you handle structured documents like tables, code blocks, or markdown files during the splitting phase to avoid breaking semantic meaning?
12How do you load and split documents lazily (streaming) to handle very large files without running out of memory?
13What is a SemanticChunker and how does it differ from fixed-size character-based splitting?
14How do you preserve and propagate source metadata (filename, page number, URL, timestamp) through the loading and splitting pipeline?
15How do you choose the right embedding model — what tradeoffs exist between OpenAI embeddings, Cohere, HuggingFace, and local models like nomic-embed?
16What is the difference between dense embeddings and sparse embeddings (BM25) — when would you combine both in a hybrid search?
17How do you handle embedding model upgrades in production — what happens to your existing vectors when you switch models?
18How do you efficiently batch embed a large corpus of documents without hitting rate limits or memory constraints?
19What are the tradeoffs between vector stores like Pinecone, Weaviate, Chroma, pgvector, and FAISS — how do you choose for production?
20How do you implement namespace or tenant isolation in a vector store for a multi-tenant RAG application?
21How do you handle incremental updates to a vector store — adding, updating, and deleting documents without full re-indexing?
22What is HNSW indexing and why does it make approximate nearest neighbor search fast at scale?
23What is a similarity score threshold in retrieval and how do you use it to filter out low-confidence results?
24What is MMR (Maximal Marginal Relevance) retrieval and how does it balance relevance with diversity of results?
25What is a MultiQueryRetriever and how does it improve recall by generating multiple phrasings of the same question?
26What is Contextual Compression in LangChain retrieval and how does it reduce noise in retrieved chunks?
27What is a ParentDocumentRetriever — how does it index small chunks but return larger parent chunks to the LLM?
28What is HyDE (Hypothetical Document Embedding) and how does it improve retrieval for vague or abstract queries?
29What is Self-Query Retrieval and how does it allow the LLM to generate structured metadata filters alongside the semantic query?
30How do you implement hybrid search combining dense vector search with BM25 keyword search using EnsembleRetriever?
31What is a Re-ranker (cross-encoder) and where does it fit in the RAG pipeline after initial retrieval?
32What is the difference between Stuff, MapReduce, Refine, and MapRerank document chain strategies — when do you use each?
33How do you build a Conversational RAG chain that maintains chat history and reformulates follow-up questions into standalone queries?
34What is query decomposition and how do you break a complex multi-part question into sub-queries for better retrieval?
35How do you implement Step-Back Prompting in a RAG pipeline to improve retrieval for highly specific questions?
36What is CRAG (Corrective RAG) and how does it add a grading step to decide whether retrieved docs are relevant before answering?
37What is Self-RAG and how does the LLM decide when to retrieve, whether retrieved docs are relevant, and whether the answer is grounded?
38How do you implement a fallback strategy when retrieval returns no relevant documents — how do you avoid hallucination in this case?
39How do you implement RAG evaluation — what metrics like faithfulness, answer relevancy, and context recall do you measure using RAGAS?
40How do you detect and mitigate hallucination in RAG outputs — what role does citation and source grounding play?
41How do you build a citation system that maps each sentence in the LLM's answer back to the exact source chunk it came from?
42How do you handle multilingual RAG — embedding and retrieving documents in multiple languages for a global user base?
43How do you optimize retrieval latency in production — what caching, pre-fetching, or index optimization strategies do you apply?
44How do you implement access control at the retrieval layer — ensuring users only retrieve documents they are authorized to see?
45How do you handle long context RAG — when retrieved chunks exceed the LLM's context window, what strategies do you apply?
46How do you design a RAG pipeline with LangGraph — turning retrieval, grading, and generation into discrete stateful graph nodes?
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Models
Messages
Tools
Middleware
Memory
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How do you implement a fallback strategy when retrieval returns no relevant documents — how do you avoid hallucination in this case?

Implement fallback strategies: set a similarity threshold to detect empty retrieval, use web search as secondary source, or have the LLM respond with "I don't know" to prevent hallucination.

When retrieval returns no relevant documents, the LLM is at risk of hallucinating an answer. To prevent this, implement a fallback workflow: first, check if any document's similarity score exceeds a predefined threshold. If none does, you can either use a secondary retriever (e.g., a web search API), or directly instruct the LLM to return a safe message like "I don't have enough information to answer that." This preserves user trust and avoids generating false information.[reference:12][reference:13]

Implementing Fallback with Similarity Threshold
Using LangGraph for Conditional Fallback
Fallback Strategies Summary
  1. 1

    Similarity Threshold: Set a minimum relevance score; reject documents below it.

  2. 2

    Confidence Scoring: Use a small LLM grader to evaluate document relevance.

  3. 3

    Web Search Fallback: Integrate a search API (Tavily, Bing) to fetch external data when internal retrieval is empty.

  4. 4

    Parametric Knowledge: Allow the LLM to use its training data only when explicitly instructed, but with a strong warning.

  5. 5

    Re‑generation with Different Query: Rewrite the query and try retrieval again (e.g., using MultiQueryRetriever).

Difficulty: 7/10
Topics: fallback strategies, hallucination mitigation, LangChain retrieval

Scenario Questions

0-2 years experience
  1. 1

    You have a simple LangChain QA chain that pulls documents from a vector store. If the similarity search returns an empty list, how would you modify the chain to provide a safe fallback response without fabricating answers?

  2. 2

    What would happen if you let the LLM generate an answer when no documents are found, and how can you detect and prevent that in code?

  3. 3

    Write a short Python snippet that checks for empty retrieval results and returns a default "I don't have enough information" message.

2-5 years experience
  1. 1

    In a production feature you need to combine multiple fallback mechanisms (e.g., a generic knowledge base and a "I don't know" prompt). How would you orchestrate these in LangChain, and what trade‑offs would you consider?

  2. 2

    During testing you notice the system sometimes still hallucinates even when the fallback is triggered. Walk me through how you would debug the chain to find why the LLM is ignoring the fallback.

  3. 3

    If the fallback pulls from a secondary, slower index, how would you handle latency and consistency concerns while keeping the user experience acceptable?

5-8 years experience
  1. 1

    Design a scalable retrieval‑fallback architecture for a multi‑tenant SaaS that serves thousands of concurrent queries. How would you isolate fallback logic per tenant and ensure hallucination is minimized across all tenants?

  2. 2

    What metrics would you instrument to monitor fallback usage and hallucination rates, and how would you use them to trigger automated alerts or model retraining?

  3. 3

    Explain how you would implement a circuit‑breaker pattern for the fallback path to avoid cascading failures when the secondary source is down.

8+ years experience
  1. 1

    At the organization level, you need to decide whether to embed fallback handling in each LangChain chain or to abstract it into a shared library/service. What are the long‑term maintenance and governance implications of each approach?

  2. 2

    How would you migrate an existing monolithic QA system that currently hallucinates on empty retrievals to a micro‑service architecture with a centralized fallback policy, while ensuring backward compatibility?

  3. 3

    Discuss the trade‑offs of using LLM‑based self‑critique versus rule‑based fallback for hallucination mitigation across multiple product lines.

Follow-up Questions

  • ❓Can you sketch the minimal Python code that implements this check?
  • ❓How would you verify that the fallback never produces fabricated content?
  • ❓What edge cases might still let hallucinations slip through?