Track Details

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

04.07.25 - 04.11.25
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

04.14.25 - 04.16.25
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.

04.28.25 - 05.07.25
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.

06.16.25 - 08.20.25
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

09.15.25 - 10.15.25
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

10.20.25 - 11.24.25
Advanced Agent Engineering

    - Write effective system prompts for regularized outputs or tool use - Design and implement agents capable of using, creating, and managing tools - Develop agents with autonomous action capabilities, including scheduling and event-triggered responses - Utilize open-source tool hubs designed for Large Language Models - Manage and economically host large vector stores - Construct self-improving agents that can evolve their prompts - Create and manage swarms of agents collaborating on complex goals - Design meta-swarms and information hierarchies for advanced agent collaboration and secrecy - Evaluate and create benchmarks for LLM performance analysis