While LangChain tries to be everything,
LlamaIndex tries to do ONE thing well: RAG.

And it succeeds.

🧩 What LlamaIndex Does Well

• indexing documents
• chunking intelligently
• building vector stores
• building retrieval pipelines
• plugging in any LLM
• plugging in any embedding model
• observability + eval tools

It abstracts the boring parts of RAG without hiding too much.

🧠 Why Developers Prefer It

LlamaIndex provides: - clear APIs
- reliable building blocks
- fewer breaking changes
- simple mental models
- good documentation
- easier debugging

It feels like “the framework LangChain wanted to be”.

🏗 Example Architecture

A typical LlamaIndex app:

Loader → Splitter → Embeddings → Vector Store → Retriever → LLM

You can assemble or replace any part.

🚀 Should YOU Learn It?

Yes — if you plan to build RAG systems.

GhostFrog could even use LlamaIndex one day for: - product notes
- niche classification
- multi-source embeddings

LlamaIndex is what I’d call “the practical RAG toolkit”.