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AI

AI in Python – applications with LLM, GPT, 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%).

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Who it's for

Ideal for teams that…

1 Python developers who want to create AI applications using the OpenAI API and GPT models
2 NLP specialists and data scientists interested in working with large language models
3 Analysts and chatbot/voice assistant creators leveraging LLMs
4 Professionals responsible for automation and process optimization with AI
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

✓

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

Program

What we actually do

  • · 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
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief
✓First session within 30 days
✓Pilot before the full decision
✓VAT invoice, payment in instalments possible

How we handle your data

We process your business data (name, work e-mail, phone number, company, job title) in order to handle your corporate training inquiry and prepare an offer. The data controller is infoShare Academy Sp. z o.o., Al. Grunwaldzka 472B, 80-309 Gdańsk. Providing the data is voluntary but necessary to receive a response. You have the right to access, rectify, erase or restrict the processing of your data and to object to it. Full information is available in our data processing notice.

Optional marketing consents

The controller of your personal data is infoShare Academy sp. z o.o. The rules for processing personal data are set out in the Privacy Policy and the Data Processing Notice.

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