Online or in-house trainings within Europe
All workshops in English or German
Full days or half days
Content and schedule adapted to your needs

Learn the basics of language models and how to build a question-answering system for your own content.

This is why you should participate in the workshop (for developers)

• Avoid weeks of studying examples and code on the Internet to find out what works best
• To learn on your own is not for everyone, instead join an interactive session with an expert trainer and a group of participants
• Boost your career and stay up-to-date with the latest AI technologies

This is why you should participate in the workshop (for decision makers)

• Thinking about introducing AI to your next project? Evaluate new technologies in short time and find out what works best for your goals
• Avoid mistakes in the beginning of projects and save costs
• Educate your team and your company with the latest technologies in order to stay competitive

Schedule

2 full days or 4 half days

Description

In this course you will learn the basic steps to implement state-of-the-art solutions with LLMs and RAG. We will deep-dive into the how LLMs process data in order to get an understanding how to apply them in an optimal way and avoid their drawbacks. LLMs are not just black boxes that we add to a software architecture and hope for the best, but with expertise we can adapt and improve the data processing within the model and their communication with a software environment.

We will have a closer look at the current ecosystem of tools and libraries that helps to kickstart a project based on LLMs. Libraries like LangChain, LlamaIndex, RouteLLM and others employ certain techniques that we will study and that will help you to either use those libraries more efficient or implement similar strategies in your projects without any additional dependencies.

You will also learn about how you can use retrieval-augmented generation (RAG) to implement a question-answering system based on your own content like websites and documents. We will implement a RAG solution from scratch and compare the implementation to available low-code solutions on the market. Evaluation and testing is a large part to build a production-ready solution, so we will study existing approaches and apply state-of-the-art evaluation metrics on our implementation to allow you to improve the answer quality iteratively.

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