LLMOPs
- Data Loader : csv to docs , then store in vector
- Prompt Template : they are instructions to LLM (give output in this way)
- Two Pipelines 1) Train 2) Recommend (or Train)
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Agentic AI Course : Week 1
8 weeks https://edwarddonner.com/2024/11/13/llm-engineering-resources/ 6 week Agentic AI Course https://github.com/ed-donner/agents the file .env has all the keys select kernel https://github.com/ed-donner/agents/blob/main/1_foundations/1_lab1.ipynb import os openai_api_key = os.getenv( 'OPENAI_API_KEY' ) from openai import OpenAI openai = OpenAI() messages = [{ "role" : "user" , "content" : "What is 2+2?" }] response = openai.chat.completions.create( model= "gpt-4.1-nano" , messages=messages ) print (response.choices[ 0 ].message.content) Day 2 : what are AI Agents? 5 different patterns for agentic systems https://lavnish.blogspot.com/2025/05/essential-design-patterns-for-building.html In General Day 3 comparing different models https://www.vellum.ai/llm-leaderboard syntax to call diff LLMs openAI , anthropic, gemini, deepseek, groq https://github.com/ed-donner/agents/blob/main/1_foundations/2_lab2.ipynb...
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