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AI255: Introduction to Large Language Models and Their Integration

Training: Artificial Intelligence

The workshop provides the fundamentals of large language models, their functionality with tokens, context, and knowledge, as well as integration into custom applications. The course covers prompt engineering, the use of relevant data, embeddings, vector databases, and API integrations of various providers. Participants learn how to correctly integrate outputs into their applications.

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Possible course languages: German

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Unfortunately there are currently no available appointments.
Would you like to request an appointment? Then click on 'No matching appointment?'

Request prefered appointment period:

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Agenda:

Introduction to Large Language Models (LLMs)

Functionality of LLMs: sequential generation, tokens, context, implicit and explicit knowledge

Fundamentals of prompt engineering

Introduction to embeddings as core component

Techniques of knowledge injection and introduction to vector databases

Presentation of various LLM providers and integration via API

Many practical exercises for application of learned concepts

Final discussion: How can the learned content be applied in your own context?

Objectives:

Integration of large language models, deployment of embeddings and vector databases, effective utilization of prompts, API integration of various LLM providers.

Target audience:

System Architects
Developers
IT Professionals

Prerequisites:

AI200 Introduction to Python for Data Science and AI (alternatively basic knowledge in Python)
AI250 Introduction Prompt Engineering for Developers

Description:

In the workshop AI255 Introduction to Large Language Models and their Integration participants learn the essential aspects of integrating large language models (LLMs) and how they can effectively deploy them in their applications. The training covers the functionality of LLMs, such as sequential generation, tokens, context and implicit as well as explicit knowledge.

Participants receive an introduction to prompt engineering and learn how they can find relevant data and insert it into prompts as well as correctly integrate outputs of the language models back into their applications. Additionally, important concepts such as embeddings and vector databases are covered and various LLM providers as well as their integration via API are presented.