Role Prompting:
Learn how to assign a role or style to the response (for example: teacher, guide, or storyteller).

Different Output Formats:
Understand how to get answers in formats like lists, tables, summaries, or detailed explanations.

Creative Prompting Methods:
Techniques like “Least to Most,” “Explain It Like I’m Five,” and “Meta Prompting” to simplify or expand responses.

Handling Long Answers:
Ways to overcome limits on length and get complete information without cutting off.

Writing Clear Instructions:
Learn to give detailed steps, set boundaries with delimiters, decide the length, and guide the answer clearly.

Step-by-Step Thinking & Context:
Use prompts that encourage logical step-by-step answers and request extra context when needed.

Advanced & Coding Prompts:
Explore methods like pre-warming chats, handling token limits, prompt injection, and automatic prompt engineering (for coding use).

Getting Started with OpenAI:
Learn about OpenAI features, how to create an account, set up an API key, and explore the Playground to try things out.

Exploring the Platform:
Understand the layout of OpenAI’s platform, different services offered, and how responses and messages work.

Working with Coding Notebooks:
Discover how to use coding notebooks to test different OpenAI services in one place.

Counting & Managing Tokens:
Learn what tokens are, how to count them, and how to manage message history properly.

Handling Responses:
Find out what streaming means, how to deal with limits and retries, and the difference between chat completions and responses.

Using Tools & Functions:
Understand how to call tools (functions) and even build a simple agent that can use them effectively.

Improving Performance:
Learn how to handle many requests at the same time using parallelization to make processes faster.

Understanding Vector Embeddings:
Learn how information can be turned into numbers so that similar ideas or words can be grouped together.

Information Retrieval with Vector Databases:
Discover how special databases store these numbers and quickly find related information when needed.

Vector Databases with Pinecone:
See how one popular database (Pinecone) works to organize and search information efficiently.

RAG with Supabase PGVector (Coding):
Explore how another tool (PGVector in Supabase) can be used for combining stored knowledge with fresh data.

Role & Emotion Prompting:
Learn how to shape responses by giving a role (like teacher, coach, or expert) and how to bring emotions or tone into the output.

Thinking Methods:
Explore ways to guide step-by-step reasoning, including “chain of thought” and learning from examples inside the conversation.

Advanced Reasoning:
Understand methods like self-consistency sampling (checking multiple answers) and ReAct (reasoning combined with taking action).

Personas of Thought:
See how different thought “styles” or personalities can be applied to get varied perspectives.

Prompt Optimization:
Learn how to refine, test, and adjust prompts to get more useful and consistent results.

Structured Outputs:
Discover how to organize answers into clear structures like lists, tables, or defined formats.

Caching & Real-Time Use:
Find out what prompt caching is, how it works in practice, and explore a real-time example with OpenAI tools.

Getting Started with LangChain:
Introduction to what LangChain is and how to install it on your system.

Working with Chat Models:
Learn how to use chat models and set up prompt templates for smoother conversations.

Handling Outputs & Large Texts:
Explore streaming responses, organizing outputs with parsers, and summarizing big pieces of text.

Managing Documents:
Understand how to load documents, split them into parts, and tag them for easy use later.

Tracking & Hubs:
Learn how to trace your work with LangSmith and share or find resources through the LangChain Hub.

LCEL – Building Blocks:
Discover the LCEL system: working with chat models, message history, building multiple chains, and using branching, merging, and configurable fields.

Databases, Agents & Tools:
See how LangChain connects with vector databases, indexing, and how to create agents that can use different tools.

Introduction to LangGraph:
Get an overview of LangGraph and how it works.

Building Simple Flows:
Learn how to create basic flows to connect steps in a process.

Tools & Data Handling:
Understand how to use tools within LangGraph and keep information saved for later use.

Working with State:
Explore how to update, manage, and customize the “state” that tracks progress during a flow.

Special Features:
Learn about time travel (reviewing past states) and self-corrective RAG for improving results.

Extra Exploration (Advanced Branching/Subgraphs):
Additional advanced topics you can explore in your own time.