Vanlev Brazil Systems · Technical FAQ
Questions about engineering AI for production.
Clear answers on architecture, RAG, agents and the practices required to operate AI applications with confidence.
What does it take to move a generative AI application into production?+
Beyond a model, teams need a clear use case, data and permissions, architecture, integrations, quality criteria, testing, monitoring and operations. The right scope depends on product context and risk.
When should we use RAG instead of fine-tuning?+
RAG is often a fit when answers need to draw on private information that changes or requires traceable sources. Fine-tuning may help adapt behavior or output patterns. They solve different problems and can be combined when there is a clear need.
How do you measure the quality of a RAG system?+
Use a representative evaluation set, assess retrieval and answer quality separately, check citations and track unanswered cases. Criteria should reflect real use, not only examples selected for a demo.
What makes an AI agent different from a chatbot?+
A chatbot can respond in a conversation; an agent can also select and execute tools to complete a task. That autonomy requires explicit boundaries, least-privilege permissions, action validation and risk-appropriate human oversight.
What is MCP and when should we use it?+
MCP is a protocol for connecting AI applications with tools and context sources through a standardized interface. It can simplify integration and tool lifecycle management; direct APIs remain appropriate for many systems.
How should LLM applications be tested?+
Combine deterministic software tests with quality, safety and behavior evaluations over representative cases. Track prompt, model and data versions so results can be reproduced and regressions investigated.
What does observability mean for AI?+
It means understanding the path of a request: relevant inputs, retrieval, tool and model calls, outputs, latency, cost and errors—while respecting privacy and access controls.
Is conversational AI a separate service?+
Conversation is one interface for an AI application, not necessarily a separate service. The important questions are what task it solves and whether the system behind it can respond with quality, context and clear boundaries.
Have a use case?
Let’s assess the system behind the idea.
Talk to Vanlev about your data, quality requirements and path to production.