Prompt Engineering - Fall (T & TH 5PM Pacific)

- **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

- 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

- 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

- 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

- 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

- 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

- 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

- 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