Artificial Intelligence (AI)

Learn the essential principles and best practices for designing robust, reliable, and effective AI agent systems that deliver real business value.

Discover how smolagents provides a lightweight, flexible framework for building AI agents without the complexity of larger solutions. Learn how to create powerful agents with minimal code and maximum control.

Learn how to run the impressive DeepSeek R1 language model on your own hardware using its distilled version and SGLang. Step-by-step guide for running one of the best LLMs on affordable hardware.

Struggling with LLM application debugging? This guide shows you how to use Opik for end-to-end tracing, monitoring, and evaluation. Learn practical techniques to make your AI applications better, with detailed examples and best practices.

Discover how to create AI agents using n8n—use a no-code/low-code platform to build robust workflows for AI and beyond.

Learn how to extend your advanced AI agent with memory to maintain conversation history and context, ensuring more coherent and context-aware responses.

Step up your LLM-powered AI agent with multiple iterations, user clarification requests, and dynamic state management. Perfect for more complex tasks.

Comprehensive tutorial for no-framework AI agent development using direct LLM interaction. Build smarter, more adaptable AI systems without the constraints of pre-built tools

Build a RAG system with Azure AI Search: This step-by-step guide shows you how to integrate retrieval-augmented generation with Azure AI Search. Learn how to build more accurate and context-aware AI applications by combining retrieval and generation.

Azure OpenAI is one of the most used AI platforms for enterprise users. The Azure environments is relatively well-trusted and provides access to all OpenAI LLM models. By default, Azure offers 'Content Filters' for their AI API. What are these and how to properly use them to net get in your way?

Learn how to set up and use Crawl4AI's web scraping capabilities using Docker. This step-by-step tutorial shows you how to set up, configure, and deploy your first AI-powered web crawler in minutes. Free, open-source, and faster than many paid alternatives.

Searching for the right documents and chunks is the most important aspect of a RAG application. However, different chunking methods come with different drawbacks. Using long-context embedding models, we can use 'late chunking' to get the best chunking method for search quality so far.

Learn how to use DSPy to automate and optimize your LLM prompts. This step-by-step tutorial shows you how to build more reliable AI systems without manual prompt engineering and serves as introduction to DSPy.

Looking for an open-source alternative to ChatGPT or Open WebUI? LibreChat offers a versatile, free AI platform that supports multiple AI models like OpenAI, Anthropic or self-hosted ones.. It’s fully customizable, secure, and perfect for self-hosting. Learn in this post how LibreChat stands out as a flexible, privacy-focused AI chat solution.

LLMs can use Tools to interact with other technology, like databases or APIs. Creating such tools however requires to semantically match the semantics of the LLM with the tool. Learn here how langchain-autotools makes this very easy.

Learn how to crawl websites and extract data perfectly prepared for LLM usage. We'll introduce Firecrawl - a powerful tool to crawl websites and extract data from these sites. We'll also demonstrate, how to use Firecrawl in combination with LangChain

Learn how to effectively evaluate and upgrade your AI models using Azure AI Studio and Prompt Flow, making sure that the new model does not introduce any regressions.

Learn how RouteLLM navigates prompts to cheap or premium models, balancing your budget and brainpower needs.

Google BigQuery recently got support for vector embeddings. Learn how to use them and how to create embeddings of your texts without leaving BigQuery. No external systems required.

Learn how to use GPT-4o to parse data from even the most complex of documents - multi-column PDFs, excel documents, tables, and more.

GPT-4o has phenomenal vision capabilities. Learn how to use the OpenAI API to send images to GPT-4o and use it's vision capabilities to describe the images, label them or provide analytical outputs from them.

Open Source, amazing community, with a user-friendly interface, easy setup via Docker or Kubernetes, and advanced features like pipelines and Retrieval Augmented Generation (RAG), Open Web UI is a great ChatGPT alternative.

Explore how Vertex AI Model Garden simplifies AI model deployment. This guide demonstrates deployment of Llama 3.1 and Claude 3.5 Sonnet, showcasing the platform's approach to hosting advanced AI models.

Learn how to create your custom GPT-4o mini model using OpenAI's finetuning API - for free!

Learn to create knowledge graphs for RAG with Neo4j LLM Knowledge Graph Builder in this easy step-by-step guide. Increase the quality of your RAG model with this modern approach to data modelling.

Simplify complex BigQuery data analysis with a custom conversational interface. Learn how to use OpenAI's GPT-4o model to generate SQL queries and execute them against your BigQuery database.

Learn how to finetune your own LLMs with modern tools like QLoRA and flash attention, even on consumer hardware - from creating a dataset, executing the fine-tuning process, and using this new model.

The more we work with AI, the more we need to extract data from documents. In a scalable manner. While conventional and machine learning based methods provide good results for most cases, especially when the data is rather technical and provides complicated layouts like multicolumn pdfs or tables, these methods often fail. Let's test whether LLMs can help us here.

AI is transforming the way we create applications and interact with data. Almost every application needs some form of AI nowadays. Every data interaction leverages some sort of AI feature. In this article, we demonstrate, how one can use AI directly from a PostgreSQL database, with only needing SQL commands.

MLPs are the backbone of the the most powerful AI models we know, but are KAN the next revolutionary step which will replace them?

LLMs have transformed the way we interact with information. However, they also pose a safety & security risk. In this blog, we introduce Llama Guard 2, a safety tool that helps you secure your LLMs.

AI and especially LLMs have been tremendously successful in generating text. In this blog post, we will show you how to use AI to create a PowerPoint presentation. And even how to tell AI to respect your PowerPoint template

Google's new Vertex AI Agent Builder simplifies the creation and deployment of AI-powered agents, making AI development more accessible to businesses and developers. This blog describes its capabilities by creating a Slack RAG application.

Microsofts 1-Bit quantization approach which boosts performance and efficency without compromising output quality

In this guide, we'll explore how to build an AI chatbot that interfaces with Confluence data, using Airbyte and Langchain for data integration and PGVector for storing our text embeddings. This practical walkthrough aims to provide clear, step-by-step instructions to enable you to set up and deploy your own LLM-powered chatbot

HyDE is a technique used to improve the performance of RAG models by generating hypothetical document embeddings based on the query and using them to retrieve relevant documents from the knowledge base.

Since 2022 GenAI changed how we are working and already assists us in many ways. But is GenAI the revolutionary pathway to our future of worklife or is their carbon footprint the shurtcut to our ecological apocalypse?

LLMs have become everyday tools for many businesses and individuals. However, the costs of running these models can quickly add up. In this guide, we will explore some strategies to help you save on LLM costs.

OpenAI has released its new embedding models, which have been trained using the new MRL technique. Discover how this not only enhances the accuracy of semantic searches but also can improve the performance of vector databases

Explore the architecture and advantages of Retrieval Augmented Generators (RAG), the innovative technology powering PondhouseAI

Finding the right documents during retrieval is probably the most important aspect of your RAG pipeline. This guide demonstrates how to use 'reranking' for

With recursive retrieval, RAG can generate more coherent and contextually relevant responses. This guide introduces you to the concept of recursive retrieval and how to implement it with llamaindex.

Running your own LLM provides a lot of flexibility and control over your data. This guide introduces you to the seamless integration of vLLM and Caddy web server, enabling HTTPS encryption for a robust, private AI environment.

RAG pipelines are one of the corner-stones of modern AI applications. Evaluating there performance is detrimental for making them robust and production ready.

Strategies for enhancing AI with corporate data