Course Outline
- 1. Intro to LLMs
- 1.1 Intro to AI engineering
- 1.3 LLM architecture
- 1.4 Tokens & context windows
- 2. Prompt Engineering
- 2.1 Intro to prompt engineering
- 2.2 Improving responses
- 2.3 Prompt techniques
- 3. Using LLM Tools
- 3.1 Top LLM tools
- 3.2 Open source LLMs
- 3.3 Getting started with ChatGPT
- 3.4 Building a custom GPT
- 3.5 LLM settings
- 4. Intro to the OpenAI API
- 4.1 Chat Completions API
- 4.2 Assistants API
- 4.3 GPT function calling
- 5. Intro to the Claude API
- 5.1 Getting Started with the Claude API
- 5.2 Using the Claude metaprompt
- 5.3 Claude function calling
- 6. GPT Fine Tuning
- 5.1 Intro to GPT fine tuning
- 5.2 Fine tuning for brand tone of voice
- 5.3 Fine tuning for structured output
- 7. Vector Databases
- 7.1 Intro to embeddings & vector databases
- 7.2 Top vector databases
- 7.3 Website Q&A with GPT-4 & Pinecone
- 8. Intro to AI Agents
- 8.1 What are AI agents?
- 8.2 Top AI agent startups
- 8.3 Getting started with with AutoGen
- 8.4 Data visualization agents with AutoGen
- 9. AI Engineering Projects
- 9.1 Text-to-speech with OpenAI & Streamlit
- 9.2 GPT-4 Vision for data analysis
- 9.3 Financial statements assistant
- 9.4 Stock screener assistant
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