Practical Application of LLMs
Hands-on LLM use cases — sentiment analysis, summarization, fine-tuning, RAG and building AI agents.
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What's inside
19 sections- 1 Table of Contents
- 2 Module 1
- 3 LLM Architectures and Capabilities
- 4 Key Factors Influencing Performance and Cost
- 5 Deploying LLMs via APIs
- 6 Demo — Sentiment Analysis with Python and OpenAI
- 7 Self-hosting Open-source LLMs
- 8 Module 2
- 9 Demo — Text Summarization with LLMs
- 10 Fine-tuning for Specialized Applications
- 11 Understanding RAG
- 12 Demo — RAG System for HR
- 13 Module 3
- 14 Understanding the Basics of AI Agents
- 15 Frameworks and Systems for Building AI Agents
- 16 Demo — Building an AI Agent for Social Media
- 17 Extractive vs Abstractive Approaches with LLMs
- 18 General Summary
- 19 Resources and Useful Links
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Evaluate open-source LLMs for performance, usability, licensing and practical deployment constraints.
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Integrating Open Source LLMs
Integrate LLMs with the OpenAI Agents SDK, RAG, vector stores, moderation and session history.
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