This tailored AI training program is designed to integrate advanced language models (LLMs) into your team's daily workflows, providing immediate efficiency gains in content production, monitoring, research, data visualisation and processing. By leveraging AI-powered instruments, structured verification, and collaborative optimization, your team will significantly reduce manual workload, enhance data accuracy, and accelerate production Significantly, your team will gain a practical framework for working with various models and master its application in professional workflows. You'll learn AI tools’ capabilities and limitations, and will be able to develop reliable strategies for their implementation.
Key Insights
Anyone who has taken a keen interest in the digital transition — or attended in person — knows that adapting to new technology takes time. It takes years to go from the initial steps of finding a domain to achieving significant viewership metrics, from monitoring trending topics to acquiring the skills to use the digital environment for information gathering and investigations. AI is the next chapter. What will it bring to your editorial team?
In the past three years our team delivered AI-Newsroom, to many media organisations and individuals. This is what we’ve learned
Key insights
Professional Development. Journalists seek to enhance technical skills while maintaining editorial standards, adapting to the changing media landscape and integrating new technologies into their workflow.
Operational Efficiency. Applicants aim to optimize work processes, particularly important for independent media operating with limited resources in challenging circumstances.
Knowledge Sharing. Strong emphasis on sharing learned skills with colleagues, multiplying impact within organizations and broader media community.
Quality Journalism. Focus on maintaining high journalistic standards while adopting new tools, particularly crucial for independent media working in exile or under pressure.
Program Structure
I Onboarding Format
Two online sessions during one week or one full day in a live classroom. Outcome: The group develops a shared prompt library and gathers ideas on how to adapt neural networks to their tasks—from news production to investigations.
II AI in Practice
Self-Directed Learning: Your team determines the further program and choose “Learning Journey” which suits their needs the most. During 4-6 “learning by doing” sessions we will learn and experiment together.
Final Project: The possibility to present an individual or group project to “defend your thesis.” at the end of the training.
Learning Journey
Core Areas
Working with Texts and Data
Multimedia
Automation
Fine-Tuning and Customization
Building on foundational understanding of LLMs your team selects focus areas based on specific needs and priorities. The following modules are available - we'll concentrate on the areas most relevant to your team.
Working with Text and Data
Language Models & Prompt Engineering
• Understand and utilize tools like Chat GPT and Claude • Master basic and advanced prompt engineering techniques
Quality Control and Safety
• Fact-checking methodologies • Strategies for handling misinformation • Ethical considerations • Data security • Data anonymization
Text Processing & Management
• Manage large volumes of text • Data safety • Automate text parsing and extraction using libraries (e.g., Beautiful Soup)
Data Analysis & Visualization
• Computer vision in dailly routine tasks • Computer vision in dailly routine tasks investigative journalism • Implement data visualization techniques
Multimedia
Content Creation:
• Create and enhance illustrations, video, and audio with AI • Transcribe Audio and video • Dubbing workflows • Learn generative techniques for visual storytelling • Generate images • Generate video • Create presenter-bot
Workflow Integration:
• Advanced enhancement techniques • Best practices for multimedia productio • Integrate multimedia tools into your production workflow • Rapid prototyping and project turnaround strategies
Automation
Process Automation:
• Automate routine editorial tasks • Develop and refine prompt libraries for consistent outcomes
Tool Integration:
• Connect AI tools with existing systems (e.g., email, messengers, schedulers, Google Docs) • Use automation platforms like Zapier and GitHub
Fine-Tuning and Customization
Data-Driven Workflows:
• Parcing • Set up APIs for seamless data processing • Implement automated reporting and real-time data analysis • Agents: enabling collaboration between multiple models in one process
Model Customization:
• Fine-tune pre-trained AI models on your own datasets • Adapt models to reflect your editorial style
Personalized AI Tools:
• Create custom AI assistants tailored to your team’s needs • Integrate specialized tools into existing workflows
Our Approach
We use a dynamic learning methodology that balances structured guidance with practical experience. Theory sessions are short and focused, immediately followed by hands-on practice with real business cases. This approach helps teams develop both shared understanding and sustainable AI implementation skills - crucial in the rapidly evolving landscape of AI tools and capabilities.
Prague School Media has been offering customized remote training in practical LLM implementation for teams for over two years. Personnel from major media organizations, think tanks, and institutions have completed this program, including BBC, Hromadske, Mizzima, Radio Liberty, CYENS Centre of Excellence, Animaccord Production, Belarusian Investigative Center, Free Press Unlimited, and FPEE.Based in Oxford, Ms. Ponirovskaya and Mr. Lvovsky conduct in-person trainings for UK-based organizations.In the same timeframe, over 1,000 individuals have completed our remote courses, enhancing their expertise in AI applications.
Methodology
We follow the Kaospilot methodology, which emphasizes: • Preparation of clear methodological materials • Practical exercises during sessions • Active participant engagement (minimizing lecture time) • Alternating theory with hands-on practice This approach helps the group develop a common language regarding AI and reflect on their practices so that their skills remain relevant even as tools evolve rapidly.
Course Instructors
Stanislav Lvovsky: Poet, historian, researcher Doctoral student at the University of Oxford, graduated from Moscow State University and Shaninka (Master’s in public history). Before his academic career, he worked in advertising, cultural management, and journalism. Currently a postdoctoral researcher at the University of Helsinki.
Zlata Ponirovskaya: AI-Adoption facilitator, Head of Prague School Media.
Toolbox
Recommended by the school, a list of tools and platforms for which our course instructors provide guidance and consultation:
Language Models (LLMs): Claude, GPT-o1, GPT-4.0, Gemini
Research Tools: Perplexity, Research Rabbit, Consensus, iAsk
Personalisation and Automation: Custom GPTs, API Assistants, Fine Tuning
Cloud GPU: Google Colab for most tasks; Runpod, Rundiffusion for heavier loads
Generative Music Platforms: Suno, Udio
Multimodality: Whisper, Pyannote, ElevenLabs
Diffusion-Based Image and Video: Fal.ai, Сivitai, ComfyUI, Midjourney, Stable Diffusion, Flux, Runway, PikaLabs, Kling, Sora
Program Outcomes
Individual Competencies:
Prompt engineering expertise
Deep understanding of AI-tools landscape and models capabilities
Process optimization using language models
AI-enhanced problem-solving techniques
Team Benefits:
Shared understanding of AI capabilities and limitations
Collaborative AI workflows and knowledge base
United approach to AI safety and ethics
Sustainable practices for continued AI adoption
Course Format & Deliverables
A three-week intensive course of six online sessions and ongoing group support, designed for teams of up to 30 participants. You'll leave with a comprehensive AI implementation package: custom prompt library, proven workflow templates, team adoption guidelines, and a practical integration roadmap. Delivery Format - Video conference.
Course Price
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