Unlock GenAI: Your 2-Step Guide to Getting Started

I constantly get asked, "How do I even start learning about GenAI?" Well, here's your roadmap – a two-step guide to get you going:

Step 1: Become a Prompt Engineer

The key to unlocking GenAI's power is mastering prompt engineering. Think of it as learning the language to talk to super-smart AI. The best way to learn? By doing!

Jump into one of these free playgrounds and start experimenting:

Setting Up the Playground

• Groq is a managed cloud based LLM platform that offers [Groq] you might as well use ChatGPT or any other LLM Chat provider of your choice

Learning about Prompting Techniques

• Zero-Shot Prompting: This technique involves asking the LLM to perform a task without providing specific examples. Example: "Translate the following sentence into Spanish: The weather is beautiful today."
• Few-Shot Prompting: In this approach, you provide the LLM with a few examples to demonstrate the desired output format or style. Example: "Translate the following sentences into Spanish: English: The weather is beautiful today. Spanish: El clima es hermoso hoy. English: I enjoy drinking coffee in the morning. Spanish: ?"
• Chain-of-Thought Prompting: This technique encourages the LLM to break down complex tasks into smaller, logical steps, resulting in more accurate and coherent responses.Example: "Jane has 3 apples. John gives her 2 more. How many apples does Jane have now? Let's think step-by-step: Jane starts with 3 apples. John gives her 2 more. So, Jane now has 3 + 2 = 5 apples."

You can find more in-depth information about these techniques and other advanced prompting strategies here: [Prompting Guide].

Step 2: Become a GenAI Developer

Once you've got the hang of prompting in a playground, it's time to build something real. The LlamaIndex framework (Python-based) is your toolkit:

• Re-create what you did in the playgrounds, but programmatically: This solidifies your understanding.
• Unlock Retrieval Augmented Generation (RAG): LlamaIndex lets you connect your GenAI to external data sources (databases, files, etc.), making your applications incredibly powerful.

This is just the beginning! The world of GenAI is vast and exciting. Keep experimenting, keep learning, and who knows what you'll create!

Setting Up the Playground

• Getting Started with Python in VS Code [VSCode IDE]
• Jupyter Notebooks [Jupyter - Ultimate Guide - VSCode Plugin ]

Programing Language and Framework

• Python is the Programming Language I recommend to engage with AI but other languages like TypeScript works as well [Python Tutorial]
• LlamaIndex is a powerful framework that simplifies the development of LLM-powered applications by providing tools for data connection, indexing, and querying. [LlamaIndex Portal]
• Groq also offers API access, allowing you to integrate its powerful LLMs into your Python applications. [Groq Console]

Prompting Programmatically

• Get a Groq API Key to use from your Python / LLamaIndex Code [Request API Key]
• Groq & LlamaIndex for invoking LLM and your own Prompts Integration [Tutorial LLM/Prompting with Groq]

Retrieval Augmented Generation

Enabling applications to access and utilize external knowledge sources. LlamaIndex facilitates the implementation of RAG by providing seamless integration with various data sources.

• High-Level Concepts [RAG Concepts]

LLM Agents and Tools

LLM agents are what's next. AI agents built on large language models control the path to solving a complex problem.

• Simple Intro with Calculator Tools [Tutorial ReAct Agent]

Other References

• Llama3 Cookbook with Groq
• Building a RAG Chatbot using Llamaindex, Groq with Llama3 & Chainlit