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

03.26.24 - 03.28.24
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

04.02.24 - 04.04.24
Custom GPTs

    - Develop a complex prompt template - Develop a rubric after rigorous testing of inputs - Create a custom GPT that can retrieve knowledge from documents - Create a custom GPT that can retrieve knowledge from a public API - Design interaction protocols that enable responsible use of large language models - Create a Custom GPT that can upgrade your other Custom GPTs

04.23.24 - 05.30.24
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

06.18.24 - 06.27.24
Intro to Agents

    - Write effective system prompts for various applications - Create and manage personas for large language models - Develop advanced summarization techniques for lengthy documents - Engineer agents capable of knowledge retrieval and self-reflection - Benchmark and enhance model performance over time - Use multiple GPTs for complex conversational scenarios - Fine-tune GPT models for specific responses and output formats

07.09.24 - 07.11.24
Agent Engineering - OpenAI Toolchain

    - Create OpenAI Assistants - Manage swarms of OpenAI Assistants - Integrate OAI-formatted functions into OpenAI Assistants - Handle tool use by OpenAI Assistants - Apply practical strategies for assistant development - Explore advanced integration techniques

07.30.24 - 08.01.24
Agent Engineering - Open Source Toolchain

    - Create a Langchain Agent - Host an Agent on Gradio - Add a Langchain Tool to an Agent - Add a Langchain Tool to OAI Assistants Using Langchain - Create a Swarm of Langchain Agents

08.06.24 - 08.27.24
Advanced Agent and Tool 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

10.21.24 - 12.12.24
Swarm Architecture

    - Master the integration of OpenAI Assistants in an Autogen Swarm - Utilize Langchain Agents and tools within Autogen - Implement UserProxy for automated interactions - Design and deploy multi-agent swarms for complex tasks - Develop critical agents for quality control and feedback - Craft and execute scripts for dynamic, multi-task conversations

10.29.24 - 11.21.24
AI Alignment

    - Develop a benchmark to track and improve AI model and prompt performance over time. - Use moderation models to evaluate and score harmful AI outputs. - Train and prompt engineer AI models towards or away from specific values. - Create a values-evaluation model through self-consistency. - Understand and discuss tokens, values, ethics, reward misspecification, and scalable oversight. - Apply techniques to reduce AI hallucination, ensure AI confidentiality, and detect sleeper agents.