KI-Tutorials

This article presents a practical guide to implementing TNT-LLM, a framework that automates taxonomy generation and text classification using LLMs, demonstrated through a news article clustering example with GPT-4 and LangGraph.

Prompt chaining is a technique that involves breaking down a complex task into a series of smaller, interconnected prompts, where the output of one prompt serves as the input for the next, guiding the LLM through a structured reasoning process.

This tutorial guides you through DuckDB's key features and practical applications, including building tables, performing data analysis, building an RAG application, and using an SQL query engine with LLM.

Explore Cohere Command R+ online and locally, learn about the unique features of the Cohere Python API, and build a multi-step AI agent using LangChain and Cohere.

A Comprehensive Guide to Reducing Model Sizes

To connect through the Claude 3.5 Sonnet API, obtain your API key from Anthropic, install the anthropic Python library, and use it to send requests and receive responses from Claude 3.5 Sonnet.

LangGraph is a library within the LangChain ecosystem that provides a framework for defining, coordinating, and executing multiple LLM agents (or chains) in a structured and efficient manner.

One-hot encoding is a technique used to convert categorical data into a binary format where each category is represented by a separate column with a 1 indicating its presence and 0s for all other categories.

Learn about the Groq API and its features with code examples. Additionally, learn how to build context-aware AI applications using the Groq API and LlamaIndex.

To connect to the Codestral API, obtain your API key from Mistral AI and send authorized HTTP requests to the appropriate endpoint (either codestral.mistral.ai or api.mistral.ai).

Learn how to implement knowledge graphs for RAG applications by following this step-by-step tutorial to enhance AI responses with structured knowledge.

Prompt compression is the process of reducing the length of an input prompt while retaining the essential information needed for a language model to understand and generate a relevant response.