ModelRefs / Model Selection Guides
Model Selection Guides
Choose models by use case, capability, constraints, and implementation trade-offs.
Overview
This section holds 2 decision guides, each with a step-by-step framework, the trade-offs it forces, and the sources behind it.
Model Selection Guides
Choose models by use case, capability, constraints, and implementation trade-offs.
How to choose an AI model for RAG
A practical framework for selecting a generation model for retrieval-augmented generation based on task fit, grounding, evaluation, latency, cost, and deployment constraints.
Level: intermediate · About 10 to read
How to evaluate AI model quality
A practical evaluation framework that goes beyond headline benchmarks to combine task-specific tests, human review, automated metrics, regressions, safety, latency, cost, and failure analysis.
Level: intermediate · About 12 to read
Other decision-guide sections
- Provider Selection Guides — Compare provider options, deployment paths, APIs, pricing factors, and operational constraints.
- Benchmark Interpretation Guides — Understand benchmark results, limitations, evaluation context, and practical relevance.
- Workflow Implementation Guides — Plan and implement AI workflows such as RAG, agents, fine-tuning, and evaluation.
- AI Architecture Guides — Design AI systems, integration patterns, data flows, and deployment architectures.
- Governance Guides — Apply governance, risk, review, and monitoring practices to AI implementation decisions.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Model Selection Guides.