How to combine vector search, fine-tuned lightweight models, and targeted agent tooling into a resilient production backend.
Most production AI failures happen because engineering teams treat RAG, Fine-Tuning, and AI Agents as mutually exclusive choices.
They pick one hammer and try to solve every problem with it.
When you rely solely on RAG, you end up stuffing 40-page PDFs and massive prompt instructions into a single context window. Your latency climbs past 4 seconds, your token bill explodes, and the model still fails to return valid JSON.
Conversely, if you try to fine-tune your way out of the problem, your model bakes in stale data. The moment your pricing or API contracts change next week, you are stuck retraining weights.
