Many AI agents can be built successfully using just a base model and good prompting. But what happens when that's not enough? How do you make an agent understand your internal context, your proprietary data, or speak in your unique style?

This is where specialization comes in. This talk is a deep dive into the two fundamental strategies for it: RAG (Retrieval-Augmented Generation) and Fine-tuning.

RAG (Retrieval-Augmented Generation): We will explore how to implement a RAG pipeline. This means setting up a vector database, search, and context retrieval. This is the best method for giving an agent access to dynamic, easily updated external knowledge (like company docs or user data) without modifying the model itself.

Fine-tuning: Next, we will analyze Fine-tuning. This method "bakes" knowledge into the model, making it static. It is also the primary way to change the model's fundamental behavior and style (e.g., teaching it your tone of voice or a specific response format).

The central question of the talk is: RAG or Fine-tuning? We won't just compare them; we'll provide a clear decision framework:

When to use RAG for dynamic, easily updatable knowledge?

When to use Fine-tuning for static facts or core behavioral skills?

And how (and why) to combine both approaches to create the ultimate specialized AI agent.

Attendees will leave with a practical guide to choosing the right specialization strategy for their AI application.