Questions
46 of 46
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?
langchainlangchain
Basics
Agents
Models
Messages
Tools
Middleware
Memory
MCP
Multi Agents
RAG
Context Engineering
Human in the loop
46 / 46

How do you design a RAG pipeline with LangGraph — turning retrieval, grading, and generation into discrete stateful graph nodes?

Difficulty: 7/10
graph orchestration, retrieval grading, state management

Design a RAG pipeline in LangGraph as a state machine with nodes for retrieval, relevance grading, query rewriting, and generation, using conditional edges to decide whether to continue, retry, or fall back to web search.

LangGraph transforms RAG pipelines from linear sequences into intelligent state machines. The core graph nodes include: retrieve (fetch documents from vector store), grade (evaluate document relevance using LLM grader), rewrite (rephrase query when retrieval fails), generate (produce final answer). Conditional edges route based on grading results: if documents are relevant, proceed to generation; if not, trigger query rewrite and retry; if multiple retries fail, fall back to web search. This creates a self-correcting system that automatically recovers from retrieval failures without manual intervention.

LangGraph RAG Pipeline Implementation
LangGraph RAG Nodes
  1. 1

    Retrieve node: Fetches documents from vector store based on query

  2. 2

    Grade node: Evaluates relevance using LLM grader; filters irrelevant documents

  3. 3

    Rewrite node: Reformulates query when retrieval fails; maintains retry counter

  4. 4

    Generate node: Produces final answer from relevant documents

  5. 5

    Web search node: Fallback when local retrieval repeatedly fails

Scenario Questions

0-2 years experience

  1. 1Suppose you need to build a simple RAG pipeline using LangGraph where you have a retrieval node, a grading node, and a generation node. How would you wire these nodes together and pass the retrieved documents between them?
  2. 2If the grading node returns a low relevance score, what would you do in the graph to handle that before calling the generation node?
  3. 3What state variables would you store in the graph to keep track of the user's query and the retrieved chunks?

2-5 years experience

  1. 1You implemented the retrieval‑grade‑generate graph, but you notice the generation step sometimes receives empty documents. Walk me through how you would debug the graph and what changes you might make to the node definitions.
  2. 2Explain the trade‑offs between doing the relevance grading inside the same LangGraph run versus calling an external evaluator service. How does that affect latency and error handling?
  3. 3How would you modify the graph to support fallback to a second retriever if the first returns fewer than three documents?

5-8 years experience

  1. 1Design a scalable RAG pipeline with LangGraph that can handle thousands of concurrent queries, ensuring each node’s state is isolated and does not cause memory leaks. What architectural patterns would you use?
  2. 2Discuss how you would implement caching for the retrieval node and how you would invalidate cache entries when the underlying knowledge base updates.
  3. 3If you need to add a post‑processing step that re‑ranks the generated answer based on business rules, where would you place that in the graph and how would you manage state transitions?

8+ years experience

  1. 1Your organization wants to migrate from a monolithic LangChain RAG implementation to a modular LangGraph architecture across multiple teams. How would you structure the graph definitions, versioning, and deployment pipelines to minimize disruption?
  2. 2Consider long‑term maintenance: how would you design observability, logging, and alerting for each LangGraph node to detect degradation in retrieval quality or generation hallucinations?
  3. 3What strategies would you employ to ensure that changes to the grading logic do not break downstream generation nodes across different product lines?

Follow-up Questions

  • What would you monitor to know the grading node is underperforming?
  • How would you test the graph’s branching logic in CI?
  • Can you describe how you’d roll out a new grading model without downtime?
Share

Share via WhatsApp, X, Facebook, LinkedIn or copy link. Open Graph preview enabled.