Thoughts, tutorials, and insights on AI, automation, and modern software development.
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Learn how to get started with LangChain using Python. This guide covers model invocation, prompt templates, LCEL chains, streaming responses, structured outputs, custom runnables, and agent creation step by step.
TOON is a token-efficient data format for LLMs. Learn how it compares to JSON, why it saves tokens, and how to use it with Semantic Kernel in .NET.
Fix Nginx errors like 413 Request Entity Too Large, 504 Gateway Timeout, and static file issues in Docker using simple nginx-proxy configuration tweaks.
Explore MarkItDown, Docling, and Mistral Document AI performance when converting complex PDFs into structured Markdown. This comparison highlights each tool’s strengths and limitations, from basic text scraping to AI-powered table recognition.
Learn what Context Engineering is, how it differs from Prompt Engineering, and see real-world examples like AI coding assistants. A simple guide for beginners to understand how AI truly becomes smart.
Learn why using a single Semantic Kernel for multiple agents causes plugins to leak between them, uncover the root cause, and explore simple solutions to ensure each agent’s plugins remain properly isolated.
Learn how to integrate Dapr sidecars into your .NET Aspire projects with Microsoft’s Semantic Kernel Process Framework to run AI-powered workflows at cloud scale. This step-by-step guide covers installation, configuration and sample code to get resilient, actor-based processes running anywhere.
Explore how Selection and Termination Strategy Functions in the Semantic Kernel Agent Framework for .NET manage multi-agent conversations by choosing the next speaker and deciding when to end the chat.
Build intelligent, event-driven workflows using Microsoft’s Semantic Kernel Process Framework to automate content creation, onboarding, and complex tasks with AI and custom logic.
Explore how multi-agent systems enable AI agents to collaborate seamlessly, enhancing efficiency and scalability. Learn to implement this approach using Microsoft's Semantic Kernel, orchestrating specialized agents to automate tasks like email drafting and approval.
This blog covers why your Semantic Kernel AI agent may ignore plugins and how to fix it with a simple configuration change.
Learn how to implement a Model Context Protocol (MCP) server using C# and integrate it with Semantic Kernel to enhance AI assistants with external data and tools through a standardized protocol.
Explore the creation of intelligent AI agents with the Semantic Kernel Agent Framework
MCP enables LLMs to access real-time data and execute dynamic actions with ease, reducing redundancy and maintenance overhead through standardized client-server interactions.
Boost your coding efficiency with ChatGPT/Gemini/Claude integration in Visual Studio! Learn how the free ChatGPTExtension eliminates tab switching, automates code fixes, and streamlines your workflow right inside your IDE. Maximize productivity and simplify your development process today!
How we enhanced our product classification system by incorporating Retrieval-Augmented Generation (RAG) to provide dataset-specific context, resulting in improved performance and reliability.
How we enhanced our product classification system by incorporating Retrieval-Augmented Generation (RAG) to provide dataset-specific context, resulting in improved performance and reliability.
A comprehensive exploration of Response Format compatibility issues when working with Llama, Phi, and Mistral model families in Microsoft Azure AI Studio with Semantic Kernel, and how to effectively navigate these limitations.
Understanding the differences between OpenAI and Azure OpenAI and how to use them with Semantic Kernel
Learn how to create a simple recommendation system for e-commerce using vector embeddings and Gradio.
A deep dive into the real costs and limitations of gpt-4o-mini for image processing tasks.
Learn how to use the new WaitFor and WaitForCompletion methods in .NET Aspire 9 to manage dependencies in your distributed applications.
A new way to call functions using the latest Semantic Kernel SDK
A Simple Guide to Structured Outputs with Semantic Kernel. Converting text responses into typed data with JSON schema and C# model classes.
Managing Azure OpenAI API versions in Semantic Kernel, including both new and legacy approaches.
Understanding JSON Mode and Structured Outputs in OpenAI Models
Writing a basic Byte Pair Encoding (BPE) tokenizer using C# and .NET
An in-depth exploration of tokenization, tokens, and their crucial role in modern LLMs
Using Semantic kernel to showcase how NLP tasks like text classification can be done with just a bit of prompt engineering in both python and .NET
An introduction to the Python version of the Semantic Kernel SDK
Using aspire orchestration to develop AI-powered apps locally with .NET and Semantic Kernel
An introduction to .NET Aspire, its integrated services, and the Aspire dashboard.
In-depth look at a .NET-based Food Health Analyzer app using Azure OpenAI and Semantic Kernel for intelligent ingredient and health analysis.
An overview for a simple AI powered app for checking packaged food healthiness
Working with the new prompt filter IPromptRenderFilter in semantic kernel
Working with yaml prompt templates in semantic kernel
Using the manual mode for function calling in Semantic Kernel
Using the gpt-4 function calling capabilities with semantic kernel
Introduction to function calling with OpenAI
Using a local embedding service with Semantic Kernel
Setting up a local embedding service using Python for development
Working with Azure multiple streaming responses in Semantic Kernel
Getting familiar with using OpenAI connector in Semantic Kernel
Building GPT plugins in dotnet using Semantic kernel
Using prompts effectively with Semantic Kernel
Designing effective prompts for emerging AI-powered applications.
Setting up a local qdrant server using the qdrant-dotnet library
Getting familiar with Semantic Kernel and its functionality
Introduction to Semantic Kernel the lightweight AI apps orchestrator using dotnet
Introduction to Vector Databases, the next-gen DBMS powering AI applications.
