Track Overview
This track is intended for software engineers, data analysts, product managers, and those who are comfortable with editing code.
Full-stack Prompt Engineering, from the ground up
Classes in This Track
Using Large Language Models
- **Emotional Priming**: Enhance output quality - **Structured Notes**: From vocal to formal - **Brainstorming Aid**: Ideate with AI - **Data Structuring**: Unstructured to organized - **Summarize Documents**: Condense lengthy texts - **First-Pass Editing**: AI-assisted editing - **Token Recognition**: Understand inference roles - **Model Comparison**: Differentiate AI models - **Limitation Awareness**: Acknowledge AI biases - **Hyperparameter Tuning**: Customize output - **System Prompts**: Set preferences easily - **Data Analysis**: Simplify complex analysis - **Plan Critique**: Evaluate and improve - **Language Efficacy**: Sharpen prompt performance - **Sparse Priming**: Knowledge decomposition - **Value Alignment**: Reflect human ethics - **Thought Chains**: Enhance reasoning - **Critical Skills**: Boost output reliability - **Rubric Creation**: Guide AI responses - **Expert Reviewers**: Refine AI output - **Prompt Optimization**: Expert-level prompts
Claude Model Context Protocol
Learn how to set up and use Claude Model Context Protocol, install multiple servers, configure new servers, and understand security best practices.
Advanced Retrieval Augmented Generation
Deep dive into embedding models (BERT, Sentence-Transformers), efficient vector search algorithms (FAISS, HNSW), vector database performance optimization, neural re-rankers, and techniques for training custom embedding models tailored to your specific domain.
Prompt Engineering
- Develop complex prompts and rubrics for AI artifacts
- Benchmark testing for prompts and templates
- Techniques for 'jailbreaking' large language models
- Automating and hosting conversations with AI
- Using no-code tools for AI automation and scheduling
- Coding alongside large language models
- Running open source large language models
- Aligning AI outputs to human values
- Creating metalanguages for symbolic reasoning and narrative design
Intro to Agents
- Design and implement request-response agents for various applications
- Create retrieval-augmented agents that can access and utilize external knowledge
- Develop autonomous agents that can take actions based on goals and environmental context
- Implement utility AI with different operational modes for more sophisticated agent behavior
- Build multi-process agents that leverage traditional AI tools for stable and predictable performance
- Design and prototype agent swarms for collaborative problem-solving
- Reuse and adapt existing agents for new applications and contexts
- Understand the principles of agent architecture and communication