The LLM Engineer
LLM Engineering Concepts LLM Engineering Concepts Understanding key concepts in Large Language Models (LLMs), Agents, OpenAI, RAG, and Fine-Tuning. 1. Introduction to LLMs Large Language Models (LLMs) process text using deep learning. Examples include OpenAI's GPT, Claude, LLaMA, and Falcon. from openai import OpenAI client = OpenAI(api_key="your_api_key") response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": "What is LLM?"}] ) print(response.choices[0].message["content"]) 2. Retrieval-Augmented Generation (RAG) Enhances LLMs by integrating retrieval mechanisms using vector databases like FAISS, Pinecone, and Weaviate. import faiss import numpy as np d = 128 # Vector dimension index = faiss.IndexFlatL2(d) data = np.random.random((100, d)).astype('float32') index.add(dat...