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LLM in practice – prompt engineering, API and workflow automation

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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?

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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 LLMs

  • what language models (LLMs) are,
  • transformer architecture,
  • examples of applications in business.

2. Basic knowledge of LLM

  • tokens and tokenization,
  • context window,
  • model parameters (temperature, top-p, max tokens),
  • costs of using models (API vs local models),
  • limitations and risks (hallucinations, bias).

3. Prompt engineering – basics

  • prompt structure,
  • roles (system, user, assistant),
  • instructions vs context,
  • Few-shot prompting,
  • Zero-shot vs chain-of-thought.

4. Prompt engineering – practice

  • creating effective prompts,
  • debugging prompts,
  • optimizing results,
  • standardizing prompts in the organization.

5. Tools for generating code

  • overview of tools (Copilot, ChatGPT, other AI IDEs),
  • generation, refactoring and debugging of code,
  • automation of the programmer's work,
  • limitations and good practices.

6. Integration with LLM APIs/SDKs

  • introduction to the API (REST),
  • authorization and key management,
  • integration examples (Python, JavaScript),
  • handling requests and responses,
  • cost and rate limit management.

7. Multimodal models

  • introduction to multimodality (text, image, audio),
  • examples of applications,
  • analysis of images and documents,
  • generation of multimodal content.

8. Creating embeddings

  • what embeddings are,
  • semantic representation of text,
  • similarity search (semantic search),
  • introduction to RAG.

9. Text work use cases

  • content editing,
  • translation,
  • summarization,
  • classification and data extraction,
  • automation of text processes in the company.

What are the prerequisites for participating in the training?

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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.

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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.

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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.

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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.