• AI/ML

    Recursive self-improvement of AI prompts

    November 29, 2025 - By 

    Large Language Models are probabilistic. They predict the next most likely word. When you ask them to “critique,” you populate their context window with high quality reasoning and negative constraints (eg. what not to do). The final generation is then statistically more likely to follow that higher standard because the logic is now part of the immediate conversation history. Try this: Draft: Ask for your content as usual. “Write a cold email to a potential client about our new web design services.” Critique: Dont just ask for a better version. Ask the AI to analyze its draft against specific criteria.…

  • AI/ML - Code

    Code snippet: Hello World agent using Microsoft Agent Framework

    November 22, 2025 - By 

    This code snippet demonstrates a sample code which uses Azure OpenAI endpoint to execute an LLM call. # pip install agent-framework python-dotenv import asyncio import os from dotenv import load_dotenv from agent_framework.azure import AzureOpenAIChatClient # Load environment variables from .env file load_dotenv() api_key = os.getenv("AZURE_OPENAI_API_KEY") deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT") endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") api_version = os.getenv("AZURE_OPENAI_API_VERSION") agent = AzureOpenAIChatClient( endpoint=endpoint, api_key = api_key, deployment_name=deployment_name, api_version=api_version ).create_agent( instructions="You are a poet", name="Poet" ) async def main(): result = await agent.run("Write a two liner poem on nature") print(result.text) asyncio.run(main()) .env file sample AZURE_OPENAI_API_KEY={paste your api key} AZURE_OPENAI_ENDPOINT=https://{your enpoint}.openai.azure.com/ AZURE_OPENAI_DEPLOYMENT=o4-mini AZURE_OPENAI_API_VERSION=2024-12-01-preview

  • AI/ML

    Completed: Course on Semantic Caching for AI Agents by deeplearning.ai

    November 21, 2025 - By 

    Most of us are in a transition phase from AI prototypes to production systems. Many frameworks that appeared impressive on demo servers have failed badly in real production environments. It is important to consider all architectural pillars and aspects during the design stage. Delaying these decisions only adds time and cost later. Agentic/AI consumes tokens, and token usage directly translates to monetary cost. This course offers a clear explanation of AI/agent caching techniques and shows how to evaluate the effectiveness of different caching strategies. Attend the course here: https://www.deeplearning.ai/short-courses/semantic-caching-for-ai-agents/

  • Code

    CaskIA — Kid-friendly, minimal AI Chat

    November 17, 2025 - By 

    My 3rd-grade kid has been using modern chat assistants for his hobbies a lot lately. Today he said he wanted his own AI chat bot. He tried building one with GitHub Copilot, but the chain-of-thought prompting pushed him into creating a search engine style chat since he did not realize he needed to connect an actual LLM to make it intelligent. I stepped in and helped him build a simple app using the free version of GitHub Copilot, and also explained how it works. It took about 30 minutes to put everything together. I am sharing it publicly so others…

  • AI/ML - Code

    Tutorial: A basic plugin using Semantic Kernel

    October 11, 2025 - By 

    Below source code are for Creating a basic plugin (testplugin.cs), and see how it is being called using prompt from Program.cs. This example uses Weather finding, such as “What is the weather in London on 18th June 2024?”, and it will always return a hard coded value of “29” degrees celsius. You can modify the function to do complex logic. testplugin.cs This is a very basic plugin, which I purposefully did not include any logic. Comments are added inline to explain what each line/function does. Program.cs appsettings.json I used this file to avoid hardcoding sensitive information in the source code…

  • AI/ML - Architecture - Articles - General

    Why you should use FastAPI?

    October 11, 2025 - By 

    If you are building an AI-powered application today, the way you expose your models through APIs can make a big difference in scalability and developer experience. FastAPI has become one of the most popular choices for creating robust, production-ready APIs, especially for AI and LLM-based workloads. It is fast, type-safe, asynchronous, and easy to work with, which makes it ideal for developers who want both speed and clarity. While Flask, Django, BentoML, and Ray Serve are all valid alternatives, FastAPI provides a good balance between simplicity and performance. For enterprise-level applications, however, more powerful frameworks like Ray Serve or BentoML…