AI Architecture Decision Guides

Six practical comparisons for choosing retrieval, training, processing modes, context, output formats, and agent coordination.

Choose an architecture by the failure it solves and the acceptance test it can pass. These guides compare operating choices without assuming that the more complex option is better.

RAG vs Fine-Tuning for Business AI

Choose between retrieving source material and changing learned response behavior by identifying the failure you need to fix.

Keyword vs Semantic vs Hybrid Search for AI Knowledge Bases

Choose retrieval methods with a query set that includes exact identifiers, paraphrases, and questions that should return no answer.

Batch vs Real-Time AI Processing

Match the processing mode to the business deadline, retry behavior, and how users receive the finished result.

Prompt Engineering vs Context Engineering

Distinguish instructions from the evidence, tools, state, and memory surrounding a business AI task.

Structured AI Outputs vs Free-Text Responses

Use an output contract when another system needs fields, and retain a separate check for whether those fields are true.

Single-Agent vs Multi-Agent Business Systems

Decide whether additional agents improve the work enough to justify coordination, cost, and new failure paths.