LangGraph lets RAG systems loop, branch, and self-correct — solving real failure modes a linear pipeline can't handle, like bad retrieval going unnoticed or one-size-fits-all query handling. But it's an orchestration layer, not a guarantee of better results, and it adds real engineering overhead. This guide breaks down when graph-based orchestration is worth it for RAG, where it gets oversold, and what still matters regardless of the architecture you choose.

Ollama lets you run open-weight LLMs entirely on your own infrastructure — a genuine advantage for RAG projects with strict data privacy needs, high query volume, or offline requirements. But local deployment shifts real responsibility onto your team: hardware, performance tuning, and production reliability. This guide breaks down when Ollama is the right fit for RAG, where its appeal gets oversold, and what still matters regardless of where your model runs.

Gemini's large context window, native multimodal support, and built-in grounding tools make it a genuinely strong candidate for RAG — but none of these features replace the retrieval architecture, evaluation, and engineering work that actually determines whether a RAG system performs well in production. This guide breaks down where Gemini excels, where its most-marketed features get oversold, and what still matters regardless of which model you choose.