Getting Started with LLMs – 1 Day Course in Washington, DC

Save 10% when registering 3 or more participants
Save 15% when registering 10 or more participants

Duration: 1 Full Day (8 Hours)
Delivery Mode: Classroom (In-Person)
Language: English
Credits: 8 PDUs / Training Hours
Certification: Course Completion Certificate
Refreshments: Lunch, Snacks and beverages will be provided during the session

This introductory and practical program provides a clear understanding of how Large Language Models (LLMs) such as ChatGPT and open-source transformer models work. Participants will explore model structure, prompting techniques, model fine-tuning, and integration workflows. Through guided exercises and real project demos, attendees will develop confidence in building and applying LLM-powered solutions across business functions and industry use cases.

By the end of this course, you will:

• Explain how LLMs and transformer models work
• Design clear and optimized prompts
• Customize or adapt models for specific tasks
• Integrate LLM capabilities into real applications
• Identify and evaluate business use cases
• Apply responsible governance and data standards

• Developers & Software Engineers
• Data Scientists & Technical Professionals
• Product & Innovation Teams
• Business Analysts & Consultants
• Anyone exploring applied AI workflows

Most professionals and organizations are exploring LLMs but lack the clarity and structure to use them effectively and responsibly. This course breaks down complex AI concepts into simple, practical steps you can immediately apply at work. You gain not just knowledge — but confidence to build and deploy real solutions.

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Want to build a stronger AI culture across your organization?
In-house programs are available to align with your team’s workflows, tools, security needs, and real operational use cases. Custom case studies and guided implementation can be built directly around your organization’s data and processes.

📧 Contact us today to schedule a customized in-house session:

Info:

• Key components of transformer architecture

• How LLMs learn and generate language

• Strengths and limitations of LLMs

• Icebreaker Activity

Info:

• Accessing commercial & open-source models

• Prompt design & iterative prompting

• Troubleshooting inaccurate outputs

• Activity

Info:

• When to prompt vs. when to fine-tune

• LoRA, RAG, embeddings & vector stores

• Preparing datasets for custom behavior

• Hands-On Demonstration

Info:

• API workflows for product development

• Building assistants, chatbots, and automation scripts

• Connecting LLMs to private enterprise knowledge

• Case Study

Info:

• Efficiency, decision support, and creativity applications

• Cost considerations and scaling strategies

• Evaluating ROI and success metrics

• Activity

Info:

• Privacy, compliance, transparency, and safety

• Preventing biased or harmful output

• Governance frameworks across AU / DE / UK

• Group Brainstorm Activity

Info:

• Mini Build: Apply LLM integration to a real workflow

• Define clear next steps and implementation roadmap

• Action Plan Review

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