Prompt Engineering — From Basics to Advanced Techniques
A complete prompt engineering skill path: from the mechanics of prompting, through few-shot and chain-of-thought, to structured output and systematic prompt quality evaluation — independent of any specific AI tool.
Prompting as a skill, not a trick
Effective work with language models is not about memorizing a few tricks — it is about understanding how a model interprets context, instruction, and the expected response format. The training starts with the mechanics of how prompts work — why one prompt produces inconsistent results while another consistently gives an accurate answer — before moving into advanced techniques.
From few-shot to structured output
Participants learn the techniques that, in practice, separate amateur AI use from professional use in business processes: few-shot prompting to teach the model a response format through examples, chain-of-thought for tasks that require multi-step reasoning, and structured output for generating data ready for automated processing (JSON, tables, forms).
Prompt evaluation as a process, not a coincidence
A good prompt rarely emerges on the first try. The training emphasizes systematic evaluation and iteration — testing prompts against a set of cases, measuring response quality, and building a library of proven patterns the team can reuse instead of starting from scratch on every new task.
Benefits
- Master basic and advanced prompting techniques (few-shot, chain-of-thought, structured output)
- Build prompts that return consistent, structured results (JSON, tables, formats for further processing)
- Design system prompts for specific business use cases
- Evaluate and iteratively improve prompt quality based on metrics
Who is this training for?
Prerequisites
- Basic ability to use any AI chatbot (ChatGPT, Claude, Gemini)
- No programming knowledge required
Training program
Prompting basics and how LLMs work
- How language models interpret and process a prompt
- Anatomy of a good prompt: context, instruction, format, examples
- Common beginner mistakes and how to avoid them
- Prompting differences between ChatGPT, Claude, and Gemini
Advanced techniques: few-shot and chain-of-thought
- Few-shot prompting — teaching the model through examples
- Chain-of-thought — guiding the model through step-by-step reasoning
- System prompts and assigning roles to the model
- Decomposing complex tasks into a sequence of prompts
Structured output and process integration
- Forcing responses in JSON/XML format for further processing
- Prompts for extracting data from unstructured text
- Building reusable prompt templates
- Applications of structured prompts in business process automation
Prompt evaluation and optimization
- Response quality metrics and A/B testing prompts
- Iteratively improving prompts based on model errors
- Limitations and pitfalls of prompting (hallucinations, bias, prompt injection)
- Building a library of proven prompts for the team
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from Prompt Engineering — Effective Work with ChatGPT, Claude, Gemini in our catalog?
That course focuses on the practical, day-to-day use of three specific tools. This training builds a full competency path — from the mechanics of prompting, through advanced techniques (few-shot, chain-of-thought, structured output), to systematic prompt evaluation and optimization, independent of any particular tool.
Do I need programming knowledge to attend?
No. The training is designed for both non-technical participants — analysts, marketers, managers — and developers. Techniques such as structured output are shown in a practical context, with no requirement to write code.
What is chain-of-thought and why does it improve answer quality?
Chain-of-thought is a technique where the prompt guides the model through step-by-step reasoning instead of demanding a direct answer. For tasks requiring analysis or multi-step reasoning it significantly reduces model errors — in the training we practice this technique on real business examples.
How do I make sure the model returns answers in a specific format (e.g. JSON)?
Structured output is achieved through precise format instructions in the prompt, examples of the target structure (few-shot), and in some models dedicated API parameters that enforce a schema. In the training we build prompt templates that return data ready for automated processing.
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