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Harness NLP and generative artificial intelligence
Text data offers uses beyond content generation alone. Connect NLP, generative models and business needs to design relevant processing approaches. Develop a method that combines experimentation with critical evaluation of results.
- Duration
- 1 day 7 hours
- Code
- IA036FR Code
Presentation
Natural language processing (NLP) and generative artificial intelligence are transforming how businesses interact with text data. This one-day course provides an essential introduction to these disruptive technologies. It explores foundational text analysis techniques and how large language models (LLMs) such as GPT and Mistral work, reshaping automation.
The learning journey demystifies the apparent magic of generative models. You will first learn how machines read and structure text through tokenisation and vectorisation, before exploring prompt engineering. You will discover how to guide these models effectively to avoid hallucinations and ensure relevant responses.
The approach focuses firmly on business application. Through workshops using Python libraries and LLM interfaces, you will experiment with sentiment analysis and task automation. You will leave with a clear methodology for identifying opportunities to integrate generative AI into your organisation while addressing security and confidentiality concerns.
Objectives
By the end of this course, you will be able to:
- analyse NLP principles and distinguish traditional applications from generative AI capabilities;
- describe key technical language-processing stages, including preprocessing, vectorisation and classification;
- explain how generative models, LLMs and transformers work and are structured;
- experiment with established tools such as spaCy and Hugging Face, and prompt engineering techniques;
- scope and design a business use case incorporating NLP or generative AI.
Program
Module 1: Understanding the NLP and generative AI landscape
- Defining concepts and distinguishing traditional NLP from LLMs.
- Exploring application areas: HR, legal, marketing and customer service.
Hands-on exercises
- Map potential NLP use cases within your organisation.
Module 2: Mastering fundamental NLP techniques
- Text preprocessing: tokenisation, lemmatisation and stop-word handling.
- Text vectorisation through TF-IDF and word embeddings.
- Classification and sentiment analysis methods.
Hands-on exercises
- Perform sentiment analysis on customer reviews using a Python library.
Module 3: Working with generative language models
- Transformer architecture and attention mechanisms.
- Prompt engineering principles and best practices to optimise results.
- Managing limitations: hallucinations, cognitive biases and GDPR compliance.
Hands-on exercises
- Test and iterate prompts with an LLM such as ChatGPT, Claude or Mistral to automate a business task.
Module 4: Integrating AI into business projects
- Selection criteria for open-source models versus API-based solutions.
- Methodological scoping of an NLP or generative AI project.
- Collaboration between product owners, data scientists and IT.
Hands-on exercises
- Develop a short project scope for integrating an LLM into an existing business process.
Audience
This course is intended for innovation and digital professionals, including:
- product owners and AI project managers seeking to integrate these technologies into products;
- innovation and digital transformation managers identifying strategic opportunities and assessing the technology's potential for their business;
- developers, data analysts and consultants seeking to understand how LLMs work;
- anyone involved in natural language projects.
Prerequisites
The following prerequisites apply:
- Professional background: general knowledge of project management or AI.
- Basic knowledge: basic Python is recommended for technical workshops but is not mandatory.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Combined expertise: gain a complete perspective by connecting NLP's technical foundations with recent generative AI innovations.
- Immediate practice: work directly with LLMs and analytical tools through 4 practical workshops.
- Technology-neutral approach: explore tools from OpenAI, Mistral and Hugging Face to select an appropriate solution.
- Business focus: turn technology into added value using proven project-scoping methods.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Any brand names and logos mentioned in this course description, such as ChatGPT, Mistral, Claude and Hugging Face, belong to their respective owners. Their mention for educational purposes does not constitute an endorsement or partnership.