AI / LlamaIndex Interview Questions
What is the purpose of LlamaIndex in a RAG pipeline?
In a RAG pipeline, LlamaIndex's job is to bridge the gap between raw, unstructured data and an LLM that can only reason over text placed directly in its prompt.
It does this in three stages. First, ingestion: loading documents and splitting them into smaller Nodes. Second, indexing: embedding those Nodes and storing them so they can be searched efficiently. Third, querying: retrieving the most relevant Nodes for a user's question and passing them, along with the question, to an LLM to generate a grounded answer.
Without a framework like LlamaIndex, a developer would have to hand-write chunking logic, embedding calls, similarity search, and prompt assembly separately for every project.
The three stages LlamaIndex manages in a RAG pipeline are:
Without a framework like LlamaIndex, a developer would need to:
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