Confident LLM use - You will understand how LLMs, tokens, context windows, and model parameters work, so you can choose the right tools and settings for business tasks instead of relying on trial and error.
Better prompts faster - You will learn how to structure prompts with clear roles, context, and examples, so you can get more accurate outputs faster, reduce rework, and make model behavior far more predictable.
Fewer errors and hallucinations - You will learn how to debug prompts, reduce hallucinations, spot bias, and validate model responses, helping you use AI more safely in daily work and in communication with clients.
Faster coding workflow - You will explore practical ways to use AI tools for code generation, refactoring, and debugging, allowing you to shorten routine development work and speed up common technical tasks.
API integration skills - You will see how to connect applications to model APIs in Python and JavaScript, handle authentication, requests, and responses, and build a solid base for your own AI automations.
Semantic search and RAG - You will understand embeddings, semantic search, and the basics of RAG, so you can build solutions that retrieve the right information from documents and produce more relevant answers.
Text workflow automation - You will learn how to design LLM-based processes for editing, translation, summarization, classification, and data extraction to streamline repetitive text work across your team or company.
Multimodal AI practice - You will discover how models work with text, images, and documents, giving you practical ways to extend AI use cases into visual content analysis and multi-format content creation.
Large language models form the foundation for many current generative AI solutions used in business and technology. This section explains the operating principle of LLMs, introduces the Transformer architecture, and situates the most widely used models in the context of their typical applications.
Model behavior depends heavily on how input is segmented and how generation settings are chosen. Participants work through tokens, context windows, and parameters such as temperature, top-p, and max tokens, with attention to how these settings affect response quality and predictability.
Before deploying LLM-based solutions, organizations need a realistic view of technical and operational trade-offs. This section compares API and local models, discusses hallucinations, bias, and model errors, and reviews data security practices that reduce risk in everyday use.
Effective interaction with language models starts from how instructions are composed. The material covers prompt structure, roles, context, and expected response format, then shows how zero-shot, few-shot, and prompt chaining techniques influence the reliability and usefulness of generated outputs.
As prompts scale across teams, consistency and quality become an operational issue rather than an individual skill. Here the focus is on refining and debugging prompts, setting up methods to control response quality, and creating reusable templates and prompt libraries for organizational use.
Developers increasingly use AI assistants not only to write code, but also to inspect and improve it. This section focuses on GitHub Copilot, ChatGPT, Claude, and IDE tooling in tasks such as code generation, refactoring, debugging, test support, documentation, and routine automation.
Connecting LLM capabilities with existing software involves more than sending a prompt to an endpoint. The section addresses authorization and API key handling, integration patterns in Python and JavaScript, and practical response management, including errors, usage limits, and cost control.
Working with more than plain text requires models that can interpret images, documents, and audio within one process. Participants examine multimodal data processing, image and document analysis, and business scenarios where combining these input types improves automation and decision support.
When business knowledge must be retrieved by meaning rather than exact keywords, embeddings and vector search become essential. This part explains how similarity is represented, how vector databases support semantic retrieval, and how to assemble a basic RAG pipeline for grounded responses.
Text operations, lightweight data tasks, and workflow support are combined here into practical LLM use cases. The section covers content generation, translation, and summarization, then moves to classification, information extraction, and building simple automated processes around model outputs.
Basic computer skills - You should be comfortable using a computer, a web browser, a text editor, and simple online tools, because the training is built around practical hands-on exercises.
Basic technical English - You should understand basic technical terms in English, since some tool names, parameters, API documentation, and code examples used during the training appear in that language.
Process-oriented thinking - You should be able to describe a task or process step by step, because during the training you will turn business goals into prompts, automations, and API-based solutions.
Optional coding basics - It helps if you know the basics of Python or JavaScript syntax, as this will make API integration examples easier to follow, but you do not need to be a developer to benefit.
