AI Fundamentals. Part 7. Architecture Decision Matrix

In this part of the Lecture Pavel Spesivtsev highlights that agentic systems frequently fail due to insufficient guardrails, such as a lack of proper temperature control leading to hallucinations or vulnerability to security threats like prompt injection.

Consequently, users are encouraged to avoid overengineering solutions when simpler methods may suffice.

Key approaches to consider, in order of complexity, include:

Prompt Engineering: Best suited for quick, well-defined tasks where carefully crafted prompts can achieve desired outcomes without needing more complex systems.

Retrieval-Augmented Generation (RAG): Recommended when the goal is to ground responses in specific domain data or information. It is advised that beginners avoid designing these systems from scratch and instead use established, out-of-the-box solutions to avoid common architectural failures.

Fine-tuning: Useful when consistent, specialized outputs are required and prompt engineering is insufficient. This approach is often more straightforward than building an agentic system, as it simply requires preparing a dataset of input-output samples to guide a general-purpose model.

Agentic Systems: Reserved for complex, multi-step workflows that require multiple tools and advanced reasoning. These are the most powerful, yet most expensive and complex, systems to deploy.

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