RAG, Agents, and Fine-Tuning — What Actually Works in Production (and What Doesn’t)

There’s no shortage of AI architecture diagrams showing Retrieval-Augmented Generation (RAG), agents, and fine-tuning layered together. In practice, many of these systems struggle once they hit production.

So what actually works?

RAG works — when it’s done properly

RAG is one of the most reliable ways to ground AI in real information. But production RAG systems succeed only when:

• Documents are chunked with meaning preserved
• Embeddings reflect the domain
• Retrieval quality is measured, not assumed
• Guardrails prevent irrelevant or sensitive content from leaking into prompts

Poor RAG doesn’t just reduce accuracy — it increases cost by inflating context and retry rates.

Agents need constraints

Agentic systems are powerful, but uncontrolled agents can be expensive and risky. In production, successful agent systems:

• Use explicit tool definitions
• Restrict credentials and destinations
• Log every action for auditability
• Fail safely when confidence is low

Agents should behave like well-designed software components, not autonomous experiments.

Fine-tuning should be lightweight and continuous

Full model retraining is rarely practical. Instead, parameter-efficient fine-tuning methods (like LoRA) allow teams to:

• Adapt behaviour without retraining the full model
• Roll changes forward or back safely
• Maintain multiple variants for different use cases

The most effective systems treat fine-tuning as a continuous improvement loop, not a one-off event.

The production mindset

What separates production AI from demos is discipline:

• Measure latency, accuracy, and cost
• Test prompts and workflows like code
• Monitor real usage and adjust continuously

AI works best when it’s treated as infrastructure, not magic.