Architecting tools with AI built on YOUR context will always surpass generic chatbots

Move beyond the chatbot. Learn to architect, build, and deploy intelligent applications where Large Language Models (LLMs) are a core, reliable component of your product.
In this intensive one-day workshop, you’ll move from prompt prototyping to production-grade LLM application design. Through hands-on labs and architectural deep dives, you’ll master the patterns that make these systems robust, cost-effective, and genuinely useful. Transforming the raw power of an LLM into a scalable, controllable feature.

You’ll learn how to ground LLMs in your own data, give them tools to interact with the world, and design the guardrails and evaluation frameworks necessary to ship with confidence.

Who It’s For

This workshop is designed for:

• Software Engineers & Full-Stack Developers ready to integrate LLMs into applications
• Machine Learning Engineers & AI Practitioners transitioning from traditional ML to generative AI
• Product Managers & Tech Leads scoping and designing AI-powered features
• Solution Architects designing the backend systems to support intelligent, conversational applications

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What You’ll Learn

Throughout the day, we’ll cover:

• Beyond the Prompt: The LLM Application Stack: Understanding models, orchestration, memory, and tools as building blocks.
• Core Architecture Pattern: RAG (Retrieval-Augmented Generation): Designing and implementing systems to ground LLMs in your proprietary data for accurate, relevant responses.
• Giving LLMs Capabilities: Function/Tool Calling: Teaching LLMs to execute code, call APIs, and interact with external systems reliably.
• Orchestration & Workflow Design: Using frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) to manage complex, multi-step reasoning and agentic flows.
• Prompt Engineering for Production: Moving from one-off prompts to template systems, few-shot examples, and chain-of-thought prompting for consistent outputs.
• Evaluation & Monitoring: How to measure quality, latency, and cost in production. Implementing guardrails for safety, relevance, and hallucination mitigation.
• Cost & Latency Optimization: Strategies for model choice, caching, prompt compression, and efficient embedding to manage operational expenses.
• Deployment Patterns: From serverless endpoints to containerized microservices. Managing context windows, state, and scalability.
• The Future-Proof Stack: Designing for model agnosticism and adapting to the rapidly evolving model landscape.

Format & Approach

Expect a practical, code-forward mix of:

• Architectural diagrams and decision frameworks for common use cases (chatbots, copilots, data analyzers).
• Guided, hands-on labs building a basic RAG pipeline and a tool-calling agent from scratch.
• Live evaluations of different prompting and model strategies, comparing outputs and costs.
• Case-study breakdowns of real-world LLM application architectures and their trade-offs.
• Group discussions on mitigating risks like data leakage, harmful outputs, and unpredictable behavior.

This workshop is not an academic overview of AI. It’s a builder’s workshop focused on the engineering discipline required to ship LLM applications that users can trust.

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Duration

🕒 1 day (in-company or open enrollment)
📍 Available only on-site

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Outcomes

After completing this workshop, participants will be able to:

• Architect an LLM-powered application using patterns like RAG and agentic tool-calling.
• Implement a basic, functional LLM application pipeline using modern frameworks.
• Make informed decisions on model selection, vector databases, and orchestration tools for their use case.
• Design and implement key non-functional requirements: evaluation metrics, cost controls, and performance monitoring.
• Lead the technical scoping and risk assessment for new product features leveraging generative AI.