LLM in practice – prompt engineering, API and workflow automation

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

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.

Training programme

1. Introduction to large language models LLM and generative artificial intelligence

principle of operation of LLM,
Transformer architecture,
the most popular models and their applications.

2. Mechanisms of LLM operation, tokenization, context and model parameters

tokens and context window,
temperature, top-p, max tokens,
the impact of parameters on the quality of responses.

3. Costs, limitations, security and risks associated with the use of LLMs

API vs local models,
hallucinations, bias and model errors,
data security and best practices.

4. Prompt engineering – designing effective instructions for language models

prompt structure and roles,
instruction, context and response format,
zero-shot, few-shot and prompt chaining.

5. Advanced prompt engineering, optimization and standardization of prompts in the organization

improving and debugging prompts,
controlling the quality of responses,
creating templates and prompt libraries.

6. Using AI tools to generate, analyze, refactor, and debug code

GitHub Copilot, ChatGPT, Claude and IDE tools,
generating and improving code,
tests, documentation and automation of the programmer's work.

7. Integration of LLM models with applications via API, REST and SDK

authorization and key management,
integration in Python and JavaScript,
handling responses, errors, limits and costs.

8. Multimodal models – the use of text, images, documents and audio

analysis of images and documents,
processing of multimodal data,
applications of multimodal models in business.

9. Embeddings, semantic search, vector databases and the basics of RAG architecture

creating and using embeddings,
semantic search and data similarity,
building a simple RAG solution.

10. Practical applications of LLMs in working with text, data, and process automation

generation, translation, and summarization of content,
classification and information extraction,
process automation and building simple workflows with LLMs.

What are the prerequisites for participating in the training?

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.