Beginner's Guide to LLM: Exploring AI with No Coding

Embracing the World of AI: A Beginner's Introduction to LLM Without Coding

The realm of AI is infinitely broad and is filled with numerous tools that can feel overwhelming to newcomers. With daily news detailing breakthroughs, potential risks, and innovative applications, opinions on AI are varied. Forming an educated perspective requires stepping into this fascinating world and learning about the concepts it presents.

Embarking on an Educational Journey

Long hours of self-study can be challenging, especially when distractions abound. To accelerate my learning, I took part in an AI hackathon—an event where knowledge-seekers, experts, and enthusiasts gather to learn, collaborate, and have an enjoyable time.

Hackathons provide unique experiences, merging competition with supportive environments where participants eagerly assist each other. Spending 48 hours among brilliant minds enabled me to grasp the basics and comprehend what I needed to create my first AI solution.

Realizing that not everyone can attend a hackathon, I’m sharing my insights in this tutorial to kickstart your journey into the world of LangChain and LLM.

LLM Explained for a 5-Year-Old

Imagine having a magical friend who knows countless words and can understand what people say. This friend helps with homework, answers questions, and spins engaging stories. A Large Language Model (LLM) embodies this magical friend, residing within a computer. It comprehends many words and can interpret user inputs, aiding people in finding information and accomplishing a variety of tasks.

What is LangChain?

For those inclined towards technology, LangChain functions as glue that enhances existing applications with AI capabilities. It permits the seamless streaming of large data amounts from apps into tools like chat interfaces, facilitating interaction and automation based on discovered information.

Navigating this concept without technical expertise, envision data as a flowing river—just like a power plant harnesses energy from water, LangChain unlocks data potential using various LLMs and third-party tools without necessitating the significant time and effort to build them. Think of LangChain as a breadbox of useful tools delivered for efficient deployment.

Example Scenario: AI-Assisted E-commerce Queries

Imagine you are an e-commerce business owner with a thriving shop and a loyal customer base. You desire to better understand your customers and leverage AI for support. Instead of manually sifting through analytics data and awaiting responses from programmers, you envision a scenario where you can query the database directly and receive instant insights.

This user flow exemplifies how LangChain and LLM can be utilized to enhance data accessibility, enabling anyone—even those without technical know-how—to acquire valuable information simply by typing a few words into a prompt.

Scenario Implementation Plan

Our goal is to build a chatbot capable of receiving queries, querying an SQL database, and returning concise insights. Here’s what we learned during the hackathon:

Lessons Learned During the Hackathon

Our team aimed to create a conversational bot, initially wanting to design a list of questions with expected answers and calculate a score based on responses.

This approach, however, proved overly complicated. Upon consulting with experts, we realized two critical points:

• Prompt engineering can yield remarkable results.
• Agents can handle significant workloads effectively.

By allowing the AI to navigate the process while focusing on prompt engineering, we enhanced our bot’s efficiency and effectiveness.

Prompt Engineering Explained for a 5-Year-Old

Imagine a magical toy that answers your questions and assists you with various tasks. To make this toy function optimally, you must seek answers in a special manner. Prompt engineering is the art of learning how to ask your toy questions effectively for the best responses.

Agent Explained for a 5-Year-Old

Think of an "agent" as a helpful robot friend who assists with various activities like locating toys or answering questions. An agent resides within a computer and performs tasks based on your requests.

Hackathon Final Prompt Solution

We crafted a simplified solution through advanced prompt engineering, predominantly developed by our non-programming team member, Iwo Szapar, our Senior Prompt Engineer. The outcome was nothing short of magic.

Applying the Hackathon Findings to Our Example Scenario

Sharing foundational assumptions, both the hackathon solution and example case affirm that user queries prompt a response via the LLM. The LLM utilizes the knowledge contained within our guidebook, cross-referencing scores with fixed data stored in a vector database to yield meaningful responses.

Vector Database Explained for a 5-Year-Old

Picture a large toy box; to locate a specific toy swiftly, you need a vector database—a magical map guiding you to your desired toy. Each toy is assigned a "vector", a numerical representation distinguishing it from others. Using algorithms, the database discerns similarities between these vectors, assisting in identifying toys based on user descriptions.

Teaching the vector database with pertinent details about toys creates a well-organized mapping system that enhances retrieval efficiency.

Code Explanation

This snippet illustrates a conversational AI agent named PlaybookChat, designed to interact with users and pull insights from a playbook. This agent operates with a tool called query_playbook, enabling it to extract information from a "People Managers guide".

Upon trigger initiation, the agent utilizes a language model like "ChatOpenAI" to thread together responses, with a memory component recording conversational history.

Final Solution for Our Example

We are already equipped with a viable solution. Here’s a prompt template:

Do you need a place to experiment a little more with prompt engineering? OpenAI provides you with an awesome playground to do just that! Feel free to copy&paste the code, and start exploring!

As for the remaining task, setting up a database filled with pertinent data is essential. Though detailed instructions are absent here, our extensive guide on similarity searches and vector databases offers step-by-step assistance. Check our other AI tutorials for comprehensive guidelines.

Summary

Beginning your AI journey may appear daunting, yet the core strength of AI lies in its ability to offer support through your process. Remember, the key is to "Let the AI take the wheel" while you steer it in the right direction.

Grasping fundamental concepts of prompt engineering, chains, agents, and vector databases is crucial before moving forward. Adequately understanding these ideas empowers you to achieve remarkable accomplishments!

Wishing you the best of luck in developing your AI applications!