LLM Fundamentals Workshop

Build clear, safe, and practical understanding of large language models - how they work, how to use them responsibly, and how to design solutions that deliver reliable outcomes.

Large language models (LLMs) are now embedded across modern productivity and engineering workflows. However, many organisations adopt LLM tooling without a shared understanding of what the technology can and cannot do. That creates risk: teams over-trust outputs, fail to design for data boundaries, or build solutions that cannot be evaluated and improved. A practical fundamentals baseline helps organisations adopt LLM capability with confidence and avoid costly false starts.

LW IT Solutions delivers the LLM Fundamentals Workshop to give teams a grounded, non-hype understanding of LLMs and how to use them responsibly in real environments. We cover core concepts such as tokens and context windows, prompting patterns, grounding with retrieval (RAG) at a high level, evaluation basics, and common safety and security considerations. The workshop is suitable for mixed technical and business audiences and can be tailored to Microsoft-first environments where Azure OpenAI and Microsoft copilots are part of the roadmap, without duplicating your Copilot service pages.

Talk through your requirements and leave with a clear next-step plan.

Book a discovery call

Service Overview

Highlights

Clear explanation of LLM behaviour, limits, and failure modes
Practical prompting patterns rather than theory
Coverage of safety, security, and data boundary considerations
Suitable for mixed technical and non-technical audiences
Aligned to Microsoft-first environments where relevant

Business Benefits

Create a shared understanding of what LLMs can and cannot do
Reduce risk from over-trusting model outputs through informed usage
Help teams design prompts and workflows that produce more reliable results
Improve decision-making around data boundaries and responsible use
Provide a common baseline for future AI initiatives and discussions

Typical use cases

Organisations starting to explore generative AI use cases
Teams adopting Copilot or Azure OpenAI alongside other tools
Leaders needing a grounded view of LLM risk and opportunity
Product and engineering teams planning AI-enabled features
Business units experimenting with prompts and automation

Objectives & deliverables

What Success Looks Like

Establish a practical baseline understanding of large language models
Enable responsible and informed use of LLM tooling
Reduce confusion and unrealistic expectations around AI capability
Support safer design of early AI experiments and pilots
Prepare teams for more advanced AI discovery or delivery work

What You Get

Workshop summary pack (slides or document)
Prompting patterns cheat-sheet (role-based)
Risk and guardrails checklist suitable for your environment
Optional: a short ‘LLM readiness’ checklist for data and governance prerequisites
Recommended next-step plan for deeper discovery or technical build (if desired)

How It Works

Scope - confirm audience roles, objectives, and any constraints (data, security, compliance).
Deliver - run the workshop with interactive examples and structured Q&A.
Synthesis - provide the summary pack and role-based patterns; agree next steps if required.

Engagement Options

Foundation Workshop - core LLM concepts for mixed business and technical audiences
Technical Focus - deeper coverage of prompting, RAG concepts, and evaluation basics
Executive Briefing - condensed session focused on risks, opportunities, and decisions
Team Enablement - repeated delivery for multiple teams with shared materials

Common Bundles

Customers who use this service often bundle with these services

AI & Automation Workshops
Structured AI and automation workshops to identify, validate, and prioritise use cases, producing a delivery-ready backlog with clear constraints.

Building RAG Apps Workshop
Hands-on workshop teaching teams to design and build Retrieval-Augmented Generation applications with secure data grounding, evaluation methods, and deployment-ready architectures.

Information Protection & Sensitivity Labels
Design and deploy Microsoft Purview sensitivity labels to classify data, apply protection controls, and support safer collaboration across Microsoft 365.