How to transform language models into intelligent, secure, and operable solutions
Synopsis
Using a language model is easy. Transforming it into a solution capable of querying knowledge, maintaining state, using tools, and producing effects safely requires much more than a good prompt.
In **AI Beyond Conversation**, Régys Borges da Silveira presents a practical method for designing useful, verifiable, and operable artificial intelligence systems. Rather than advocating for a universal technology, the book teaches how to find the minimal sufficient architecture for each problem — including recognizing when not to use AI or when an agent would add more risk and complexity than value.
Across 30 chapters organized in 7 parts, the reader progresses from the fundamentals of language models to prompt engineering, context, evaluation, function calling, memory, embeddings, vector databases, RAG, agents, multi-agents, MCP, APIs, workflows, and automation.
The work also addresses the challenges that separate a demonstration from a professional solution: dataset engineering, model selection and adaptation, fine-tuning, quantitative evaluation, local and cloud AI, serving, latency, costs, scalability, security, privacy, LGPD, governance, observability, and failure recovery.
Examples, analogies, exercises, contracts, and decision matrices bring concepts closer to the reality of developers, architects, data professionals, and technical leaders. Brazilian scenarios involving legacy systems, business processes, and regulated environments help transform principles into decisions applicable to the work.
The book includes access to supplementary material with executable labs, automated tests, and technical templates. More than teaching how to add AI to a system, **AI Beyond Conversation** shows how to decide what to build, for whom, with what data, under what constraints, and how to demonstrate that the solution actually works.
