LLM in Python Applications – Integration with Large Language Models Training Course
This training introduces participants to the topic of large language models (LLMs) and teaches practical integration with them using Python. The program covers both the theoretical foundations of how LLMs work and specific techniques for communicating with models via APIs and SDKs.
Participants will learn key concepts such as tokenization, temperature, context windows, and how to construct effective prompts using techniques including few-shot learning and chain-of-thought. The course explores various model access interfaces—from the OpenAI platform and ChatGPT, through direct API requests, to using the LangChain framework and the OpenRouter aggregator.
The training includes working with structured outputs, real-time streaming, and text embeddings that enable semantic document search and comparison. We emphasize practical applications: participants will implement solutions for automatic text summarization, translation, sentiment classification, and information extraction.
After completing the training, participants will be able to independently design prompts, integrate Python applications with various language models, choose appropriate techniques for specific problems, and deploy LLM-based solutions in production environments. They will gain practical knowledge enabling them to leverage generative models in real-world business projects.
This course is available as onsite live training in Poland or online live training.Course Outline
Module I: Fundamentals of Large Language Models
1. Mechanisms and architecture of generative models
2. Key concepts—tokens, temperature, other LLM parameters
3. Context window and its limitations
4. Hallucination phenomenon and strategies for minimization
5. Principles of effective prompt engineering
6. Prompt formatting techniques (few-shot learning, chain-of-thought)
7. ChatGPT interface—capabilities and limitations
8. OpenAI platform—playground, models, managing API keys
9. Differences between API and SDK
10. Overview of available models and their applications
Module II: Communicating with Models in Python
1. Basics of making API requests using the requests library
2. Official OpenAI SDK
3. Handling API responses and formatting results
4. Structured output—enforcing specific response structures
5. Real-time response streaming
6. LangChain framework
7. OpenRouter as an aggregator for accessing various models
Module III: Vector Representation of Text
1. Concept of text embeddings
2. How models understand meaning—the vector space
3. Embedding API in OpenAI
4. Measuring semantic similarity between texts
5. Practical applications of embeddings
Module IV: Practical Applications of LLMs
1. Automatic document summarization
2. Extraction of key information from text
3. Machine translation using LLMs
4. Text classification—sentiment analysis and categorization
Requirements
Open Training Courses require 5+ participants.
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