Ai Llm Optimization — Free AI & ML Tutorial
Learn Ai Llm Optimization in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Llm Optimization in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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Ai Llm Optimization in AI & ML
Large Language Models (LLMs) like GPT-4 and Claude are powerful out-of-the-box, but you can make them even better for specific tasks through fine-tuning and prompt optimization.
Fine-tuning: Train the LLM on domain-specific examples (e.g., financial documents, medical records). The model learns task-specific patterns. Requires 50-500 examples depending on complexity.
Prompt strategies: Chain-of-Thought (ask model to show reasoning), Few-Shot (give 2-3 examples), Retrieval-Augmented Generation (RAG — provide relevant documents so model stays factual).
Cost: Fine-tuning costs compute but saves tokens on every inference. RAG adds latency but keeps responses factual and current (model has access to latest information).
Ai Llm Optimization — Syntax
# Prompt optimization strategies: # 1. Chain-of-Thought: "Think step by step" # 2. Few-Shot: "Example 1: ... Example 2: ... Now solve: ..." # 3. RAG: "Here's the document: ... Now answer: ..." # 4. Role assignment: "You are a tutor. Explain in simple language: ..."
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