# Applied AI Solution Architect — FDE > An intensive, lab-driven program for professionals building an AI-enabled full-stack enterprise application Architect, build, deploy and govern an AI-enabled full-stack enterprise application end to end. - Duration: 5 days - Format: Instructor-led, lab-driven, continuous capstone project - Delivery: Online Instructor Led Workshop; Onsite Program for your Teams - Trainer: [Muhammed Shakir](https://www.linkedin.com/in/muhammed-shakir-gen-ai-trainings-and-consulting): Creator, Projekts.ai | Enterprise AI Builder & Architect | Helping Businesses Build AI-Agent-Driven Operations | Agentic AI, RAG & AI-Native Systems | Forward Deployed Engineer | Corporate Trainer - Request it or join the waitlist: https://workshopps.com/courses/applied-ai-solution-architect-fde ## Why this course exists An AI-enabled enterprise application requires software engineering, Generative AI, retrieval, agentic workflows, full-stack integration, security, responsible AI, governance, and quality. This program gives participants an end-to-end, build-first understanding of how to architect, design, develop, integrate, deploy, and govern an AI-enabled full-stack enterprise application. Participants build small, inspectable PoC(s) that shall demonstrate the core topics. --- ## Skills You'll Walk Away With - Apply modern software engineering practices, SDLC, Agile, Git/GitHub, clean code, design principles, design patterns, and code reviews. - Design prompts, LLM application flows, embeddings and vector search, RAG, advanced RAG knowledge systems, AI agents, and multi-agent enterprise agent architecture. - Build AI-powered web and conversational applications that connect AI models with enterprise APIs, databases, and enterprise knowledge systems. - Apply AI-assisted software engineering, AI application architecture, AI UX, human-in-the-loop workflows, AI security, responsible AI, governance, and evaluation. --- ## Who should attend This program is for technical professionals responsible for AI-enabled enterprise applications. - **Forward-deployed engineers** — building and integrating AI-enabled full-stack enterprise applications. - **Solution architects** — defining AI application architecture, application boundaries, technology selection, and enterprise controls. - **Software engineers** — applying modern software engineering and AI-assisted software engineering to AI-powered applications. - **Technical leads** — connecting AI application delivery with security, responsible AI, governance, and quality. --- ## Course Outline ### Day 1 — Establish the Engineering and LLM Application Foundation *Set up the capstone, define its enterprise AI use case, and make its first LLM application flow work.* #### Session 1 — Engineering & FDE Fundamentals; AI & Generative AI Foundations *Establish the software engineering and AI foundation for the continuous capstone.* - Modern software engineering practices, SDLC, Agile, Git/GitHub, clean code, design principles, design patterns, and code reviews - AI/ML overview, Generative AI, LLMs, foundation models, transformers, multimodal AI, and enterprise AI use cases - *Hands-on:* Establish the capstone as an engineering engagement and define its enterprise AI use case. - Create the capstone project in Git/GitHub and define its SDLC and Agile flow. - Record the initial application design using clean-code, design-principle, and design-pattern decisions; conduct a focused code review. - Map AI/ML, Generative AI, LLM, foundation-model, transformer, and multimodal-AI considerations to the selected enterprise AI use case. - **Session checkpoint:** a version-controlled capstone foundation with an explicit engineering/FDE approach and enterprise AI use case. #### Session 2 — Prompt Engineering; LLM Application Development *Design the prompt and context approach, then turn it into the capstone’s first LLM application flow.* - Prompt design, zero-shot/few-shot prompting, context engineering, structured outputs, and prompt optimization and evaluation - LLM APIs, model selection, token management, context windows, temperature, structured responses, and API integration - *Hands-on:* Build the initial prompt-to-LLM application flow. - Design zero-shot and few-shot prompts, then define the context-engineering and structured-output approach. - Optimize and evaluate the prompts against the intended application behaviour. - Select a model, connect the LLM API, apply token, context-window, and temperature choices, and return structured responses through API integration. - **Session checkpoint:** a working LLM application flow with documented prompts, context, structured outputs, model selection, token management, context windows, temperature, and API integration. ### Day 2 — Build the Knowledge and Agentic AI Foundation *Add retrieval, advanced RAG knowledge capability, and a controlled agentic workflow to the same application.* #### Session 3 — Embeddings & Vector Search; Retrieval-Augmented Generation – RAG *Make enterprise document content searchable and use it for grounded generation.* - Embeddings, semantic search, vector databases, similarity search, indexing, metadata filtering, and hybrid search - RAG architecture, document ingestion, chunking, embeddings, retrieval, reranking, grounding, citations, and RAG optimization - *Hands-on:* Build the capstone’s retrieval and RAG path. - Create embeddings, index them in a vector database, and implement semantic and similarity search with metadata filtering and hybrid search. - Ingest and chunk documents, then implement retrieval and reranking in the RAG architecture. - Generate grounded responses with citations and optimize the RAG flow. - **Session checkpoint:** a working document-ingestion, vector-search, retrieval, reranking, grounded-generation, and citation path. #### Session 4 — Advanced RAG & Knowledge Systems; AI Agents & Agentic AI *Extend RAG into an enterprise knowledge system and add a human-in-the-loop agentic workflow.* - Query transformation, contextual retrieval, multi-source retrieval, knowledge bases, RAG evaluation, and enterprise document intelligence - AI agents, planning, tool/function calling, memory, reasoning workflows, agent orchestration, and human-in-the-loop - *Hands-on:* Extend the RAG path and add an AI agent. - Add query transformation, contextual retrieval, and multi-source retrieval; organize the content as a knowledge base and evaluate it for enterprise document intelligence. - Define an AI agent’s planning and reasoning workflow, connect tool/function calling and memory, and orchestrate it with a human-in-the-loop step. - **Session checkpoint:** an advanced RAG knowledge system and an AI-agent workflow with visible planning, tools/functions, memory, reasoning, orchestration, and human oversight. ### Day 3 — Build the Enterprise Application and Its Integrations *Connect multi-agent capability to the AI-powered application, enterprise APIs, external systems, and enterprise data.* #### Session 5 — Multi-Agent & Enterprise Agent Architecture; Building AI-Powered Applications *Evolve the agentic workflow into an enterprise agent architecture and expose it through the application experience.* - Multi-agent systems, agent collaboration, enterprise tools/APIs, workflow orchestration, and agent governance - Integrating LLMs into web applications, AI-enabled UI/UX, chat interfaces, conversational applications, and AI workflows - *Hands-on:* Build the AI-powered enterprise application experience. - Define multi-agent collaboration, connect enterprise tools/APIs, orchestrate the workflow, and record the agent-governance approach. - Integrate the LLM capability into the web application and create the AI-enabled UI/UX, chat interface, conversational application, and AI workflows. - **Session checkpoint:** an AI-powered web and conversational application with multi-agent collaboration, enterprise tools/APIs, workflow orchestration, and agent governance. #### Session 6 — AI + API Engineering; AI + Database Integration *Connect the AI-powered application with enterprise APIs, external systems, and enterprise data.* - Connecting AI models with enterprise APIs, tool calling, function calling, API orchestration, and external system integration - Structured and unstructured data, vector databases, SQL generation, natural-language data access, and enterprise knowledge systems - *Hands-on:* Implement enterprise API and database integration in the capstone. - Connect AI models with enterprise APIs, add tool calling and function calling, and orchestrate APIs for external system integration. - Connect structured and unstructured data, vector databases, SQL generation, natural-language data access, and enterprise knowledge systems to the application. - **Session checkpoint:** AI-to-enterprise-API and AI-to-database integration across external systems, structured and unstructured data, vector databases, SQL generation, natural-language data access, and enterprise knowledge systems. ### Day 4 — Refine the Application Architecture and Human Experience *Use AI-assisted software engineering to improve the capstone, make its architecture explicit, and add AI UX, human-in-the-loop, and security controls.* #### Session 7 — AI-Assisted Software Engineering; AI Application Architecture *Refine the capstone and consolidate its architecture.* - GitHub Copilot/AI coding assistants, code generation, debugging, refactoring, test generation, documentation, and developer productivity - AI application patterns, LLM orchestration, RAG architecture, agent architecture, application boundaries, and technology selection - *Hands-on:* Improve and architect the integrated solution. - Apply GitHub Copilot/AI coding assistants for code generation, debugging, refactoring, test generation, and documentation; review developer productivity. - Map the AI application patterns, LLM orchestration, RAG architecture, and agent architecture; define application boundaries and technology selection. - **Session checkpoint:** a refined capstone with explicit AI application patterns, LLM/RAG/agent architecture, application boundaries, and technology selection. #### Session 8 — AI UX & Human-in-the-Loop; AI Security *Add human oversight to the application experience and address AI application security risks.* - Conversational UX, explainability, human validation, feedback mechanisms, escalation, and approval workflows - Prompt injection, jailbreaks, data leakage, insecure tool use, model abuse, adversarial inputs, and AI application security - *Hands-on:* Complete the AI UX and human-in-the-loop flow, then apply AI security. - Refine conversational UX and explainability; add human validation, feedback mechanisms, escalation, and approval workflows. - Identify prompt-injection, jailbreak, data-leakage, insecure-tool-use, model-abuse, and adversarial-input risks; apply AI application security. - **Session checkpoint:** an AI UX with human oversight and an explicit AI application security approach. ### Day 5 — Govern and Evaluate the Completed Enterprise Application *Apply responsible AI, governance, and quality evaluation to the completed capstone.* #### Session 9 — Responsible AI; AI Governance *Establish responsible-AI practices, governance, and enterprise controls.* - Fairness, transparency, explainability, privacy, safety, hallucination management, and human oversight - AI risk management, model governance, auditability, data governance, compliance, policies, and enterprise controls - *Hands-on:* Apply responsible AI and governance to the capstone. - Review fairness, transparency, explainability, privacy, safety, hallucination management, and human oversight in the application. - Define AI risk management, model governance, auditability, data governance, compliance, policies, and enterprise controls. - **Session checkpoint:** responsible-AI practices and an AI-governance approach integrated into the capstone. #### Session 10 — AI Evaluation & Quality *Evaluate the completed AI-enabled enterprise application.* - Accuracy, relevance, groundedness, hallucination measurement, robustness, toxicity, bias, and LLM evaluation - *Hands-on:* Evaluate the completed capstone and consolidate its end-to-end outcome. - Evaluate accuracy, relevance, groundedness, hallucination measurement, robustness, toxicity, and bias through LLM evaluation. - Review the completed application from software engineering through AI, RAG, agentic AI, full-stack integration, security, responsible AI, governance, and quality. - **Session checkpoint:** a completed AI-enabled full-stack enterprise application with its AI evaluation and quality approach made visible. --- ## Capstone Project Participants incrementally build one **AI-enabled full-stack enterprise application**. Day 1 establishes the software engineering, FDE, AI/Generative AI, prompt-engineering, and LLM-application foundation. Day 2 adds embeddings and vector search, RAG, advanced RAG and knowledge systems, and agentic AI. Day 3 turns these capabilities into an AI-powered application with multi-agent architecture, enterprise API integration, and enterprise database integration. Day 4 refines the application through AI-assisted software engineering, makes its architecture explicit, and adds AI UX, human-in-the-loop, and AI security. Day 5 applies responsible AI, AI governance, and AI evaluation and quality. The application builds in one direction. Each session extends the same capstone; participants do not replace it with a new demonstration. The final application makes AI application patterns, LLM orchestration, RAG architecture, agent architecture, enterprise tools/APIs, enterprise knowledge systems, human oversight, security, governance, and evaluation visible in the same solution. **In scope:** modern software engineering, AI and Generative AI, prompt engineering, LLM application development, embeddings and vector search, RAG, advanced RAG and knowledge systems, AI agents and agentic AI, multi-agent and enterprise agent architecture, AI-enabled full-stack development, AI security, responsible AI, AI governance, and AI evaluation and quality. --- ## A Note on Delivery The five-day sequence is deliberate: establish the engineering and LLM application foundation; build knowledge and agentic capability; create the enterprise application and its integrations; refine its architecture, human experience, and security; then govern and evaluate the completed solution. Each day contains two sessions, and each session balances explanation, demonstration, guided hands-on work, inspection, and a capstone checkpoint. --- **Ready to architect and build AI-enabled enterprise applications?** *Enquire about a private cohort or a tailored delivery for your team.*