Each test is 5 questions with varying difficulty.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.
LlamaIndex is the leading data framework for building RAG (Retrieval-Augmented Generation) pipelines and LLM-powered search systems over private, enterprise data. While LangChain focuses on LLM orchestration and tool use, LlamaIndex specialises in the data ingestion, chunking, indexing, retrieval, and synthesis pipeline, the critical layer that connects unstructured documents, databases, and APIs to foundation models.
In 2026, LlamaIndex is a core skill for AI Engineers and Data Engineers building production RAG systems. Junior engineers are expected to understand the basic pipeline: Document → NodeParser → VectorStoreIndex → QueryEngine. Mid-level engineers must reason about chunking strategies (chunk_size, chunk_overlap), hybrid search combining BM25 and vector retrieval, and reranking for precision. Senior engineers are assessed on advanced retrieval patterns: Router Query Engines for multi-index routing, Recursive Retrieval for hierarchical documents, and SubQuestion Query Decomposition for complex multi-hop queries.
This topic is essential for AI Engineers, Data Engineers building knowledge bases, and Applied AI Engineers delivering document Q&A and enterprise search products.
Over 80% of enterprise data is unstructured, locked in PDFs, emails, Confluence pages, and Slack threads. LlamaIndex provides the purpose-built toolchain to unlock this data for LLM reasoning: data connectors for 150+ sources, intelligent chunking that preserves semantic coherence, vector store integrations for scalable retrieval, and response synthesisers that ground answers in retrieved context.
The business case is direct: a well-tuned LlamaIndex RAG pipeline can replace expensive fine-tuning cycles for knowledge retrieval tasks, update the knowledge base with new documents without retraining, and provide source citations that reduce hallucination liability. In regulated industries like finance and healthcare, citation-backed answers from LlamaIndex pipelines are a compliance requirement.
As an interview signal, LlamaIndex questions reveal whether a candidate understands the full retrieval stack. Explaining why default chunk sizes degrade retrieval for structured tables, how metadata filtering reduces irrelevant context passed to the LLM, and when to use a Router Query Engine over a single VectorStoreIndex demonstrates the production depth that distinguishes AI engineers who ship reliable systems from those who only run Jupyter notebooks.
LlamaIndex follows a modular pipeline where raw data is ingested, parsed into nodes, indexed, and then queried via a retrieval-generation loop.
Data Source
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[Data Loaders]
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[Node Parsers]
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[Vector Store Index]
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[Retriever Engine]
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[Post-Processor]
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[Response Synthesizer]
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Final LLM ResponseUses a LLMSingleSelector or EmbeddingSingleSelector to dynamically route an incoming query to the most relevant sub-index (e.g., routing product questions to a product catalogue index and support questions to a knowledge base index). The router evaluates each sub-index's summary description and selects the best match.
Trade-offs: Enables multi-domain retrieval without polluting context with irrelevant results. Overhead: the routing LLM call adds latency. If descriptions are poorly written, routing accuracy degrades.
Indexes parent documents (chapters, sections) and child nodes (paragraphs) separately. Initial retrieval targets small, precise child nodes; the full parent context is then injected into the LLM prompt. Implemented via IndexNode objects that reference parent documents.
Trade-offs: Significantly improves answer accuracy for long documents by providing rich surrounding context. Cost: higher token consumption per query.
Combines dense vector retrieval (semantic similarity via embeddings) with sparse BM25 retrieval (keyword matching) using a VectorIndexRetriever and BM25Retriever. Results are fused using Reciprocal Rank Fusion (RRF) before passing to the synthesiser.
Trade-offs: Recovers precision for exact-match queries (product codes, names) that pure vector search misses. Adds complexity: requires both a vector store and a BM25 index to be maintained.
| Reliability | Implement retry logic with exponential backoff for both embedding API calls and LLM synthesis calls. Use a persistent VectorStore (Pinecone, Qdrant, pgvector) rather than the default in-memory store to survive process restarts. For ingestion pipelines, use IngestionPipeline with a document store (MongoDB, Redis) to track ingested documents and avoid duplicate processing on re-runs. |
| Scalability | Scale embedding generation horizontally using batch ingestion (IngestionPipeline with num_workers) and GPU-accelerated local models. For high-query-volume deployments, use a managed vector database with horizontal sharding (Pinecone pods, Qdrant distributed mode). Cache frequent query embeddings using GPTCache or semantic caching middleware to reduce embedding API calls. |
| Performance | Optimise retrieval latency by selecting HNSW-indexed vector stores with appropriate efSearch parameters. Apply metadata pre-filters before ANN search to reduce the candidate pool. Use async QueryEngine.aquery() to avoid blocking the event loop. For hybrid search, tune BM25 and vector score weights using Reciprocal Rank Fusion (RRF) rather than linear combination for more stable fusion. |
| Cost | Embedding cost is linear in document volume, use cheaper models (text-embedding-3-small, BGE-small) for initial indexing and reserve high-quality embeddings for query-time. Reduce synthesis tokens by applying aggressive reranking (top_k=3 after rerank) to minimise context sent to expensive frontier models. Implement semantic caching for identical or near-identical queries. |
| Security | Implement fine-grained access control at the node level. |
| Monitoring | Track retrieval latency and LLM token usage via telemetry. |
LlamaIndex is specialized for data indexing and retrieval, while LangChain is a general-purpose framework for LLM orchestration. You often use them together.
A Document is the raw input object, while a Node is a processed, chunked version of that document with metadata and relationships.
Use it when you need semantic search over unstructured text data to retrieve relevant context for RAG.
Yes, LlamaIndex has specialized indices like the SQLIndex to query structured data using natural language.
A Reranker improves retrieval precision by re-evaluating the top-k results from the initial vector search.
Optimize chunk sizes, use smaller embedding models, and implement caching for frequent queries.
LLMs often struggle to retrieve information from the middle of a long context window compared to the beginning or end.
Yes, it is widely used in production for enterprise RAG applications requiring scalable data ingestion and retrieval.
You can update nodes in the index or rebuild the index if the data changes significantly.
It is a query engine that uses a selector to route queries to the most relevant sub-index based on the user's input.
Hybrid search combines semantic vector search with keyword-based BM25 search to improve retrieval accuracy.
Use the LlamaIndex evaluation module to test your pipeline against ground truth datasets.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.