Vietnam AI partners: judge RAG and AI agent engineering, not models

The guide starts from a simple observation: calling an LLM API is quick, but running an enterprise AI system in production adds authentication, authorization, data processing, system integration and monitoring. It illustrates the gap with architecture diagrams — a basic frontend-to-LLM-API chain versus an internal document search stack that runs from data ingestion and parsing through a search index, permission checks and answer generation.

For RAG, the work breaks into three checks: how data is acquired and pre-processed across PDFs, Office files, wikis and CRM/ERP systems; how retrieval is chosen among keyword, vector, hybrid and reranking approaches and why; and how access rights are enforced in the search and application layers rather than through prompts alone. It gives the example of HR documents that ordinary employees should not be able to retrieve.

On AI agents, it stresses that giving an agent authority to operate external systems demands more careful design than a chatbot — limiting available tools, authenticating API calls, validating inputs, logging results and requiring human approval for important operations. It also notes that not every task suits an agent; stable, rule-based workflows may work better, so vendors should explain why an agent was chosen over alternatives. The same logic carries into vendor selection: company names and service pages are not enough, and buyers should ask what design and implementation a partner proposes for their actual requirements.