LLMs in the Real World – Episode 5: Tools vs RAG

Описание: When LLMs Should Retrieve Knowledge and When They Should Take Action

Not every AI question is solved the same way.
Some questions require retrieving knowledge from documents, while others require interacting with live systems.
In Episode 5 of LLMs in the Real World, we explore the difference between two key architectures used in modern AI applications:
Retrieval-Augmented Generation (RAG) and Tool-based systems.

Understanding when to use each approach is essential for building reliable, real-world LLM applications.

🎯 What you’ll learn in this episode

The difference between knowledge retrieval and system actions
What problems RAG systems are designed to solve
Why RAG cannot handle live or dynamic data
What LLM tools and APIs are
How models decide when to call a tool
Real examples of tool usage: APIs, databases, calculators
Why most production AI systems combine RAG + tools
The architecture behind modern AI assistants

🧠 Key Insight

Modern AI assistants are not just text generators.
They act as orchestrators deciding when to retrieve knowledge, when to call tools, and how to combine both to solve real problems.
Understanding this distinction is critical when designing scalable LLM systems.

📺 Series: LLMs in the Real World

Episode 1 — From Demo to Production
Episode 2 — The Hallucination Problem
Episode 3 — Context Windows & Their Limits
Episode 4 — Retrieval-Augmented Generation (RAG)
Episode 5 — Tools vs RAG
Next: Evaluating LLM Systems

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