From Individual AI Assistants to Shared Engineering Intelligence: Part 1
Why organizations need an internal AI platform that shares engineering knowledge, rules, skills, and ADRs across Claude Code, Cursor, Copilot, and future AI agents.
RAG for Large PDFs: A Navigation-Based Alternative to Vector Retrieval Using PageIndex
A practical guide comparing Vector RAG and navigation based RAG for large PDFs, showing how a hierarchical PageIndex lets LLMs locate answers inside structured documentation without relying on vector databases or similarity search.
Getting Started with LangChain: Build LLM-Powered Agents Step by Step with Python
A working LangChain v1 agent in Python: model setup, LCEL chains, structured output with Pydantic, tool-calling agents, and how it compares to Semantic Kernel.
TOON vs JSON: How Token-Oriented Object Notation Reduces LLM Token Costs
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.
PDF to Markdown Conversion Tools: Beyond the Hype - A Deep Dive into MarkItDown, Docling, and Mistral Document AI
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.
What Is Context Engineering? Boost Your AI with Better Context
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.
Why Your Semantic Kernel Agents Are Sharing Plugins and How to Fix It
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.
Integrating Dapr with DotNet Semantic Kernel Process Framework on Aspire
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.
Understanding Selection and Termination Strategy functions in .NET Semantic Kernel Agent Framework
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.
Automate Workflows with Microsoft Semantic Kernel Process Framework in .NET
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.
Building Multi‑Agent AI Workflows with Semantic Kernel Agent Framework in .NET
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.
Why Your AI Agent Isn't Calling Your Tools: Fixing Function Invocation Issues in Semantic Kernel
This blog covers why your Semantic Kernel AI agent may ignore plugins and how to fix it with a simple configuration change.
Building a Model Context Protocol Server with .NET and Semantic Kernel Integration
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.
Building AI Agent using Semantic Kernel Agent Framework
Explore the creation of intelligent AI agents with the Semantic Kernel Agent Framework
Model Context Protocol (MCP): The New Standard for AI Integration
MCP enables LLMs to access real-time data and execute dynamic actions with ease, reducing redundancy and maintenance overhead through standardized client-server interactions.
Boosting Developer Productivity with ChatGPT/Gemini/Claude Integration in Visual Studio
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!
From Prompt Engineering to RAG: Optimizing Product Category Classification Systems - Part 2
How we enhanced our product classification system by incorporating Retrieval-Augmented Generation (RAG) to provide dataset-specific context, resulting in improved performance and reliability.
From Prompt Engineering to RAG: Optimizing Product Category Classification Systems - Part 1
How we enhanced our product classification system by incorporating Retrieval-Augmented Generation (RAG) to provide dataset-specific context, resulting in improved performance and reliability.
Understanding Response Format Limitations: Why Llama, Phi & Mistral Models Struggle in Azure AI Studio
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.
Using OpenAI vs Azure OpenAI with Semantic Kernel. What's the difference?
Understanding the differences between OpenAI and Azure OpenAI and how to use them with Semantic Kernel
Building a Recommendation System Using Text Embeddings and python
Learn how to create a simple recommendation system for e-commerce using vector embeddings and Gradio.
Should you use GPT-4o-mini for multimodal tasks?
A deep dive into the real costs and limitations of gpt-4o-mini for image processing tasks.
Improved function calling with the Semantic Kernel
A new way to call functions using the latest Semantic Kernel SDK
Using Structured Outputs with Semantic Kernel
A Simple Guide to Structured Outputs with Semantic Kernel. Converting text responses into typed data with JSON schema and C# model classes.
Setting up the Azure OpenAI API versions in Semantic Kernel
Fix the "json_schema is enabled only for api versions 2024-08-01-preview and later" error, plus how to set the Azure OpenAI API version in Semantic Kernel.
JSON Mode and Structured Outputs Mode using OpenAI Models
OpenAI's Structured Outputs beats JSON Mode for schema-bound apps, but it still fails on truncated responses, model refusals, and hallucinated values.
Implementing a Simple BPE Tokenizer in .NET
Writing a basic Byte Pair Encoding (BPE) tokenizer using C# and .NET
Understanding Tokenization in Large Language Models
An in-depth exploration of tokenization, tokens, and their crucial role in modern LLMs
Building a Simple text Classifier with Semantic Kernel
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
Working with Semantic Kernel in Python
Semantic Kernel's Python SDK still lags the .NET version in features. Here's the exact code to send your first prompt, and why it got cut off at 150 tokens.
Building AI Apps with .Net Aspire and Semantic Kernel
Using aspire orchestration to develop AI-powered apps locally with .NET and Semantic Kernel
Building a .NET Food Health Analyzer with Azure OpenAI and Semantic Kernel
In-depth look at a .NET-based Food Health Analyzer app using Azure OpenAI and Semantic Kernel for intelligent ingredient and health analysis.
Packaged Food Health Checker with Semantic kernel
An overview for a simple AI powered app for checking packaged food healthiness
The new prompt filter in Semantic Kernel
Working with the new prompt filter IPromptRenderFilter in semantic kernel
Yaml prompts with semantic kernel
Working with yaml prompt templates in semantic kernel
Manual function calling with Semantic Kernel and OpenAI
Using the manual mode for function calling in Semantic Kernel
Function calling using Semantic Kernel
Using the gpt-4 function calling capabilities with semantic kernel
Introduction to OpenAI's function calling
Introduction to function calling with OpenAI
Using Semantic Kernel with local embeddings
Using a local embedding service with Semantic Kernel
Quick Setup for a local embedding Server using python
Setting up a local embedding service using Python for development
Vision in Semantic Kernel
Using gpt-4 with vision in Semantic Kernel
Multiple streaming responses with Azure OpenAI and Semantic Kernel
Working with Azure multiple streaming responses in Semantic Kernel
Working with Azure OpenAI on Semantic Kernel
Getting familiar with using OpenAI connector in Semantic Kernel
Getting Started with Semantic Kernel plugins
Building GPT plugins in dotnet using Semantic kernel
Prompt engineering with Semantic Kernel
Using prompts effectively with Semantic Kernel
What is Prompt Engineering
Designing effective prompts for emerging AI-powered applications.
Understanding Embeddings
Introduction to embeddings and their uses
Setting up a Qdrant client for .NET
Setting up a local qdrant server using the qdrant-dotnet library
Introduction to RAG systems
Brief introduction to RAG and their working
Getting Started with Semantic Kernel using .NET
Getting familiar with Semantic Kernel and its functionality
What is Semantic Kernel
Introduction to Semantic Kernel the lightweight AI apps orchestrator using dotnet
Introduction to Qdrant
Qdrant setup with Docker in two commands, plus the collection/point/payload model you need before your first vector search actually works right.
Introduction to Vector Databases
Introduction to Vector Databases, the next-gen DBMS powering AI applications.
Understanding LLMs and their workings
Understanding LLMs and their workings from the basics.
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