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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How do you handle long context RAG — when retrieved chunks exceed the LLM's context window, what strategies do you apply?

Difficulty: 8/10
chunking, summarization, context window management

Handle long context RAG using truncation (head+tail preserves beginnings and endings), query-aware chunk compression (SmartChunk predicts optimal abstraction level), or hierarchical summarization (chunk summaries → section rollups → document summary).

When retrieved chunks exceed the LLM's context window, apply multi-strategy compression. Head+tail truncation preserves the beginning and end of each chunk, which contain the most critical information. SmartChunk retrieval uses a planner to predict the optimal chunk abstraction level for each query, producing high-level embeddings without repeated summarization. For very long documents, hierarchical summarization creates local summaries per chunk, consolidates into section-level rollups, and finally generates a document summary, passing only the most relevant level to the LLM. The REFRAG framework compresses low-relevance chunks while preserving core content, achieving up to 30x speedup and 16x context extension.

Head+Tail Truncation Implementation
Long Context Strategies
  1. 1

    Head+tail truncation: Preserve beginning and end, truncate middle section

  2. 2

    SmartChunk retrieval: Query-adaptive framework predicting optimal abstraction level

  3. 3

    Hierarchical summarization: Chunk summaries → section rollups → final summary

  4. 4

    REFRAG framework: Compress low-relevance chunks while preserving core content (30x speedup)

Scenario Questions

0-2 years experience

  1. 1You have a LangChain RetrievalQA chain that returns 10 documents, each about 800 tokens, but the model's context window is 4,000 tokens. How would you adjust the pipeline to stay within the limit?
  2. 2During a test run the answer generation fails with a token overflow error after you added a new data source. What quick code change would you make to fix it?
  3. 3Explain how you could use LangChain's map‑reduce chain to handle the overflow without rewriting the whole retrieval logic.

2-5 years experience

  1. 1Your product team wants a concise answer from a knowledge base of 50,000 documents. Retrieval returns 20 chunks that together exceed the context window. Walk me through the trade‑offs between summarizing all chunks versus re‑ranking to fewer chunks.
  2. 2In a load test you notice that adding a summarization step doubles latency. How would you diagnose the bottleneck and reduce latency while still keeping within the token budget?
  3. 3We switched from gpt‑3.5‑turbo (4k) to gpt‑4‑turbo (8k) but still hit limits on some queries. What changes would you make to the LangChain retrieval pipeline to keep it model‑agnostic?

5-8 years experience

  1. 1Design a scalable LangChain component that automatically adapts chunk size and summarization depth based on the model's context window and current token usage. What pieces would you build and how would they interact?
  2. 2Our system must support multi‑turn conversations where each turn adds retrieved context. How would you prevent context blow‑up over a session while preserving the most relevant information?
  3. 3Explain how you would implement a fallback that switches to a retrieval‑only mode when summarization fails, and which metrics you would track to evaluate its impact.

8+ years experience

  1. 1At a company with several products using LangChain, you need to define a cross‑team policy for handling long‑context RAG. What architectural guidelines would you set, and how would you ensure consistency and future maintainability?
  2. 2We are migrating from a monolithic RAG service to a micro‑service architecture. How would you redesign long‑context handling to minimize coupling and allow independent scaling of retrieval, summarization, and LLM inference?
  3. 3Discuss the long‑term trade‑offs of building a custom token‑budget optimizer versus relying on LangChain's built‑in reducers, considering open‑source contributions and vendor lock‑in.

Follow-up Questions

  • What impact does on‑the‑fly summarization have on latency and cost?
  • How would you monitor and alert on token‑budget overruns in production?
  • If the chosen summarizer occasionally drops critical facts, how would you detect and remediate that?
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