- developers using AI assistants
- small businesses organizing knowledge
- teams that need safer AI adoption
Artificial intelligence
Applied AI in software development
This hub organizes practical criteria for using AI in software work. The focus is not hype. It is how teams can use AI to draft, compare, document and investigate while keeping human review, reliable sources and production risk visible.
- Which tasks can be accelerated with AI and which still need specialist review.
- How to keep generated code, documentation and specifications auditable.
- What data, documents and permissions should exist before building a RAG workflow.
- How to prevent AI from becoming a source of hidden technical debt.
Reading path
Start with the strongest context already available in the portal.
Some deeper references still point to Portuguese source articles while the full English migration continues. The English hub itself gives the decision map and the language relationship stays explicit.
Trusted sources for corporate AI
Foundation article in Portuguese about grounding and source quality.
Open referenceAI in development: productivity, risk and review
Portuguese deep dive about using AI without skipping engineering discipline.
Open referenceRAG for small business documents
Portuguese guide about preparing documents before retrieval-augmented generation.
Open referenceHow to use this hub
Treat this page as a decision map. The purpose is to help you name the problem before choosing a framework, vendor, refactor or automation. When the topic is clearer, the linked material becomes easier to interpret and compare.
The English migration is intentionally additive: the Portuguese portal remains stable, and each English page receives its own route, canonical URL and language alternates.
Terms to align before going deeper
FAQ
Common questions before acting on applied ai in software development.
Can AI write production code safely?
AI can draft code, tests and alternatives, but production safety depends on review, context, tests, dependency control and someone accountable for the final decision.
What is the first step before using RAG?
Organize the source documents, ownership, permissions, update routine and quality rules. A RAG workflow with outdated documents only automates confusion.
How should a small team start with AI?
Start with low-risk workflows such as documentation drafts, test ideas, log analysis and specification review, then measure whether the result actually reduces rework.
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