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Site title: NinethSense oWnZ mE! - Praveen's CTO Blog
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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.
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")
depl...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 us...
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 ...
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