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 load and split documents lazily (streaming) to handle very large files without running out of memory?

Use lazy_load() instead of load() to stream documents from a loader as a generator, processing each document one at a time, and chain it with a splitter using split_documents to avoid loading all data into memory at once.

LangChain provides a lazy_load() method on document loaders that returns a generator (iterator) rather than loading all documents into memory at once. This is crucial for large files or large numbers of documents. You can then iterate over the generator, and for each document, apply your splitter using split_documents. This creates a streaming pipeline where only one document chunk is held in memory at a time. Some loaders, like the database loaders, also support lazy_load() natively.

Lazy Loading and Splitting Example

This pattern is essential for handling very large documents (e.g., multi-gigabyte log files) or directories with thousands of files. Without lazy loading, the entire dataset would be loaded into memory, leading to memory exhaustion and slow performance. The lazy_load() method is implemented by all LangChain loaders that support streaming (most do). If a loader doesn't implement it, you can create a custom loader by subclassing BaseLoader and implementing the lazy_load generator method.

Difficulty: 6/10
Topics: lazy loading, document splitting, memory management

Scenario Questions

0-2 years experience
  1. 1

    Suppose you need to ingest a 5GB PDF using LangChain, but your notebook only has 8GB RAM. How would you set up the loader and splitter to process the file without loading it all into memory?

  2. 2

    If you use LangChain's TextLoader on a large text file and notice the process crashes due to memory, what change would you make to load the file lazily?

  3. 3

    Can you walk me through the code you’d write to stream a large CSV line‑by‑line and split each line into chunks for embedding?

2-5 years experience
  1. 1

    You added a custom RecursiveCharacterTextSplitter with a large chunk size to a pipeline that streams documents, but the latency spikes. What could be causing the slowdown and how would you adjust the splitter or streaming strategy?

  2. 2

    During a production run, some documents are being truncated after the split. How would you debug whether the issue is in the lazy loader versus the splitter configuration?

  3. 3

    Explain the trade‑offs between using LangChain’s DocumentLoader with a file path versus wrapping a Python generator that yields chunks. When would you pick one over the other?

5-8 years experience
  1. 1

    Design a component that can ingest arbitrarily large PDFs, split them into overlapping chunks, and feed them to an embedding model, all while staying under a fixed memory budget. What LangChain primitives would you combine, and how would you monitor memory usage?

  2. 2

    Your service must handle concurrent streams from multiple users, each uploading multi‑gigabyte files. How would you ensure that the lazy loading and splitting logic scales without causing OOM across the process pool?

  3. 3

    If you needed to support both local file streaming and S3 object streaming with the same splitting logic, how would you abstract the loader to keep the codebase maintainable?

8+ years experience
  1. 1

    At a platform level, we want to replace our current monolithic document ingestion service with a LangChain‑based streaming architecture that supports pluggable loaders and splitters. What architectural patterns would you introduce to handle versioning, observability, and graceful degradation when a loader fails?

  2. 2

    Consider a legacy system that pre‑processes documents in batch, storing all chunks in a database. How would you migrate to a lazy streaming approach with minimal downtime, and what data migration strategy would you employ?

  3. 3

    What are the long‑term maintenance implications of relying on LangChain’s built‑in lazy loaders versus implementing custom streaming parsers, especially regarding security, compliance, and vendor lock‑in?

Follow-up Questions

  • ❓What would happen if the chunk size exceeds the available memory?
  • ❓How would you test that your streaming implementation doesn't leak file handles?
  • ❓Can you describe how you’d instrument the pipeline to detect memory spikes in production?