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Hands-on gen AI workshop · you leave with working code

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 or Onsite
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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.