You’ve learned how to install LlamaIndex and have become familiar with RAG workflows. You loaded documents, built and persisted searchable indexes, and created an LLM-powered query engine to ask questions grounded in your data. You also explored swapping in different LLM backends, including OpenAI, Google, and local models via Ollama.
These skills enable you to build data-aware, AI-powered applications by grounding model responses in your private documents, thereby enhancing accuracy and reducing hallucinations.
In this video course, you’ve learned how to:
- Install and configure LlamaIndex and set LLM providers such as OpenAI, Google, and Ollama
- Load your data with LlamaIndex readers and build a searchable index
- Create a query engine and run requests against your selected LLM
- Apply RAG to get answers grounded in your own data
With these skills, you can start building RAG apps targeting your own data. Try connecting new data sources, experimenting with index types, and evaluating different models to refine your system.
