AI Architect Handbook: Production RAG & Agents with Yogana S

Most developers can call an LLM API. Very few can design a GenAI system that survives production, stays within budget and earns a client's trust.

This handbook is the playbook I use as a Senior GenAI Architect delivering AI projects for clients in the US, UK and India.

After reading this, you will be able to

• Design a RAG or agent system that holds up in production, not just a demo
• Cut LLM costs with proven optimisation patterns
• Estimate, price and propose AI projects to clients with confidence

Inside (97 pages, 12 chapters in 4 parts):

✅ AI project discovery and build vs buy vs fine-tune decisions

✅ LLM selection and model strategy

✅ RAG architecture end to end, with failure modes and fixes

✅ AI agents and multi-agent design with real examples

✅ Document AI and structured extraction

✅ LLM cost optimisation, evaluation and observability

✅ Security, governance and deployment for enterprise clients

✅ Estimating, pricing and proposing AI projects

✅ Your AI architect career roadmap

Every chapter includes an architecture diagram, a real (anonymised) project example, an architect's checklist and key takeaways.

Appendix: discovery questionnaire, production readiness checklist, glossary and tools map.

For: developers, ML engineers and tech leads moving into AI architecture or AI consulting.

Format: PDF, instant download.

FAQ

Is this for beginners? It's best if you already know basic Python and have used an LLM API.

Is there code? It focuses on architecture: diagrams, checklists and real project examples. For hands-on code, get the 5 GenAI Projects + Code kit.

Bundle tip: Handbook + Project Kit together = theory + practice.