Trainings listAI

AI in Python – Applications with LLM, GPT, and OpenAI API

Level

Intermediate

Duration

24h / 3 days

Date

Individually arranged

Price

Individually arranged

Training: AI in Python – Applications with LLM, GPT, and OpenAI API

The course AI in Python – Applications with LLM, GPT, and OpenAI API is an intensive 2–3 day program combining theory (20%) with practical workshops (80%). Participants will learn how to work with large language models, including GPT, and gain hands-on skills to build their own AI applications using the OpenAI API. The training focuses on effectively integrating LLMs into Python applications, applying prompt engineering techniques, processing various data formats, and deploying solutions based on vector databases and Retrieval-Augmented Generation (RAG) systems.

Who is this training for?
  • logo infoshare Python developers who want to create AI applications using the OpenAI API and GPT models
  • logo infoshare NLP specialists and data scientists interested in working with large language models
  • logo infoshare Analysts and chatbot/voice assistant creators leveraging LLMs
  • logo infoshare Professionals responsible for automation and process optimization with AI

What will you learn during this training?

  • How to effectively use the OpenAI API and Python libraries to work with LLMs and GPT
  • How to design and optimize prompts for high-quality outputs
  • How to build AI applications – chatbots, assistants, RAG systems with vector databases
  • How to automate tasks using AI agents and low-code tools
  • How to ensure security and scalability of AI solutions in production environments

Training Program

  • Day 1: Introduction to LLMs and working with the OpenAI API

    • Module 1: Fundamentals of LLM, GPT, and OpenAI

      • What are LLMs and how do GPT models work?
      • Overview of the OpenAI API ecosystem: capabilities and limitations (GPT-3/GPT-4, Claude, LLaMA, CodeLlama)
      • Setting up the work environment: JupyterLab, Python, open-source libraries
      • Role and functions of the OpenAI API: main features, limitations, models, and endpoints
      • Workshop: first HTTP requests to the API (POST/GET, JSON, REST)
    • Module 2: Prompt Engineering and Data Processing

      • Effective prompt techniques: zero-shot, few-shot, chain-of-thought
      • Iterative prompt refinement, negotiation, and output control
      • Formatting and extracting data from text – preparing input and interpreting results
      • Working with multiple formats: JSON, audio transcription, multimedia
      • Exercises: information extraction, analysis, and quality evaluation of responses
  • Day 2: Building Applications and Advanced AI Features

    • Module 3: Building AI Applications in Python

      • Integrating LLMs with Python applications via API (OpenAI SDK, REST, requests, frameworks)
      • Programming core components in Python (libraries: openai, streamlit, pandas)
      • Code generation, analysis, and refactoring with AI models (code assistant, code review)
      • Building a simple chatbot and interactive assistant with context and long-term memory
      • Introduction to FastAPI and Streamlit – deploying models as services
    • Module 4: Advanced Techniques – Vector Databases and RAG

      • Introduction to vector databases and vector text representation
      • Indexing documents, storing vectors, contextual search
      • Retrieval-Augmented Generation (RAG) – combining search with generative LLMs
      • Practical use of ChromaDB or other vector databases
      • Exercises: implementing a RAG module for a chatbot to enhance responses
  • Day 3: Automation, Security, and Deployments

    • Module 5: Process Automation and AI Support

      • Automation with AI agents: AutoGPT, LangChain, CrewAI – comparisons and use cases
      • Low-code/no-code integration of LLMs for extended functionality (e.g., Make, Zapier)
      • Automating code workflows – code generation, testing, and review with LLMs
      • AI applications in business: marketing, HR, finance, education
      • Developing AI applications: business logic implementation, user data personalization
      • Workshop: building simple automated AI pipelines
    • Module 6: Security, Ethics, and Production Deployments

      • Best practices for secure AI development and data protection in LLMs
      • Detecting and minimizing hallucinations and undesired outputs
      • Cost management and API usage monitoring
      • Preparing for deployment: scaling, monitoring, and log analysis
      • Workshop: “When the model generates nonsense – how to detect and fix it?”
      • Discussion & Q&A: best practices and future AI development trends

Contact us

we will organize training for you tailored to your needs

Przemysław Wołosz

Key Account Manager

przemyslaw.wolosz@infoShareAcademy.com

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