metodologia-genai-architecture

Design GenAI architecture pipelines by defining how LLMs retrieve knowledge, orchestrate models, and manage agent workflows. This provides a structured blueprint aligning RAG pipelines, vector databases, and governance for reliable, scalable AI systems.

Structure RAG pipelines by defining how queries are processed, retrieved, assembled, and validated across heterogeneous data sources. The architecture specifies retrieval design, provenance tracking, and continuous quality monitoring for production workloads.

Multi-model tiering works by specifying model tiers, cost-aware routing, and fallback strategies for production workloads. This ensures LLM orchestration remains reliable and scalable while managing computational costs and dynamic query routing.

Yes, you need guardrails and governance for agent workflow automation to ensure safe, auditable automation. The architecture details tool usage, memory patterns, and guardrails specifications to maintain control over autonomous LLM operations.

Yes, you can use this methodology to architect GenAI systems for enterprise knowledge management with dynamic connectors, provenance, and continuous quality monitoring. It satisfies requirements for retrieval design, tool integration, and evaluation metrics.

Without proper vector database design and LLM orchestration, GenAI systems lack reliable knowledge retrieval and cost-aware model routing. Omitting guardrails specification and evaluation metrics risks unauditable automation and unmanaged query failures.