LLM Engineering: From Prompt to Production
12-module course covering prompt engineering, fine-tuning, RAG, evaluation, and production deployment of LLM applications.
Key Highlights
- ✓ 12 modules with hands-on projects
- ✓ Prompt engineering mastery
- ✓ Fine-tuning techniques (LoRA, QLoRA, DPO)
- ✓ RAG system architecture
- ✓ Evaluation and monitoring
- ✓ Production deployment patterns
Overview
A comprehensive 12-module course on building production LLM applications. Covers prompt engineering, fine-tuning, RAG, evaluation, safety, and deployment.
What's Inside
Course Structure
Modules: (1) LLM Fundamentals, (2) Prompt Engineering, (3) Fine-tuning with LoRA/QLoRA, (4) RAG Systems, (5) Tool Use and Agents, (6) Evaluation Metrics, (7) Safety and Alignment, (8) Cost Optimization, (9) Latency Optimization, (10) Observability, (11) Deployment, (12) Capstone Project.
Prerequisites
Python proficiency, basic ML knowledge, and familiarity with PyTorch. No prior NLP experience required. Includes setup guides for local and cloud environments.
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