Building AI-Powered Web Applications
Key Points
- Building an AI‑powered web app is simpler than it sounds: the UI sends a question to a library or framework, which calls an LLM provider’s API with a prompt and returns the answer.
- In basic prompting you embed both the user’s question and short instructions (e.g., “be helpful, don’t hallucinate”) directly in the prompt sent to the model.
- Retrieval‑augmented generation (RAG) first converts the question into an embedding, uses a vector database to fetch the top‑N relevant documents, and then includes those documents plus the question in the prompt for a more informed response.
- RAG requires you to preload your domain data into the vector store, and you typically implement it with an open‑source library or framework rather than a raw API call.
- A newer pattern adds an AI “agent” layer that receives the question, plans and executes tool calls (via a framework), and then reflects on the results before producing the final answer.
Sections
- Building AI-Powered Web Applications - The speaker outlines a simple pipeline—user interface → library/framework → LLM API—explaining prompt construction, basic querying, and the more advanced Retrieval‑Augmented Generation approach for creating AI‑assisted web apps.
- Agent-Based AI Application Patterns - The segment outlines how AI agents plan, select tools, and execute tasks—contrasting basic prompting, Retrieval‑Augmented Generation, and single/multi‑agent frameworks for building applications.
Full Transcript
# Building AI-Powered Web Applications **Source:** [https://www.youtube.com/watch?v=xBSMBEowLcY](https://www.youtube.com/watch?v=xBSMBEowLcY) **Duration:** 00:04:29 ## Summary - Building an AI‑powered web app is simpler than it sounds: the UI sends a question to a library or framework, which calls an LLM provider’s API with a prompt and returns the answer. - In basic prompting you embed both the user’s question and short instructions (e.g., “be helpful, don’t hallucinate”) directly in the prompt sent to the model. - Retrieval‑augmented generation (RAG) first converts the question into an embedding, uses a vector database to fetch the top‑N relevant documents, and then includes those documents plus the question in the prompt for a more informed response. - RAG requires you to preload your domain data into the vector store, and you typically implement it with an open‑source library or framework rather than a raw API call. - A newer pattern adds an AI “agent” layer that receives the question, plans and executes tool calls (via a framework), and then reflects on the results before producing the final answer. ## Sections - [00:00:00](https://www.youtube.com/watch?v=xBSMBEowLcY&t=0s) **Building AI-Powered Web Applications** - The speaker outlines a simple pipeline—user interface → library/framework → LLM API—explaining prompt construction, basic querying, and the more advanced Retrieval‑Augmented Generation approach for creating AI‑assisted web apps. - [00:03:05](https://www.youtube.com/watch?v=xBSMBEowLcY&t=185s) **Agent-Based AI Application Patterns** - The segment outlines how AI agents plan, select tools, and execute tasks—contrasting basic prompting, Retrieval‑Augmented Generation, and single/multi‑agent frameworks for building applications. ## Full Transcript
A lot of web developers are using AI applications such as chat assistance or code assistance.
But building an application yourself can sound like a scary task, but it's not as complex as you think.
In this video, I'm going to walk you through how you
get from asking a question to retrieving an answer by a large language model.
There are a couple of patterns in between.
So let's break down what a typical application looks like.
Often it starts with a user interface and a user interface.
You can ask your questions.
The user interface will then connect to a library or a framework.
This library or framework could be open source or it could be a cloud product.
This library or framework will interact with an API and is APIs typically provided by an LLM provider.
As mentioned, there are a couple of better things I'd like to highlight.
Usually people start by asking a question to a large language model.
This question will be put inside of a prompt.
Then this prompt will be sent to the large language model.
And retrieve your final answer.
So in your prompt, you would put both your question, but also a set of instructions for the LLM,
such as be a helpful assistant or don't hallucinate or don't offend anyone.
There's also more complex, better and next to basic prompting and this is what we call RAG or retrieval augmented generation.
Again, it starts with a question.
This time your question won't be directly put inside a prompt, but it will be turned into an embedding.
This embedding will then be used by a vector database to find relevant context.
So it is vector database
is right here.
And with this vector database, you can retrieve relevant context.
We call this top N matches.
These top N matches will be put inside a prompt.
And this prompt will, of course, also contain your question.
What the LLM sees is your prompts, which includes both the question and the top N matches.
And based on this, it's going to return your final answer.
With RAG is also a stage where you upload your data into the vector database.
And this is important because otherwise the vector database won't be able to retrieve early and relevant context.
If you look at basic prompting, this is typically done via an API or an SDK, which could be provided by LLM provider.
If we look at RAG or retrieval augmented generation, you typically do this via a library or a framework.
So the final pattern and you can implement as a web developer on building your application is AI agents with AI agents.
You still have a question and a final answer,
but it's time you have an agent in the middle that will help you to answer the question.
So we start again with a question.
This time your question will be sent to the agent.
There are multiple patterns to implement agents, and typically you use a framework or a library to do this.
The agent will typically plan based on your question and the available tools.
It will act or react based on the tool calls.
And finally, it's going to reflect and see if your answer is matching the question.
For the plan the next stage it needs a set of tools.
And these tools could be either APIs, databases or code, for example, to crawl the web.
The LLM is going to use those tools to plan and execute.
And finally, providing you with your final answer.
Next to a single agent.
You can also have a multi agent framework.
This typically involves a supervisor agents, which is going to determine
which agent should be called to answer your question.
So in this video we looked at three different paterns to implement AI applications.
The first one was basic prompting, where you use a prompt, which includes your question and a set of instructions.
The second one was RAG, Retrieval Augmented Generation,
where we use a vector database to make LLMs context aware of your data.
And a final one was agents, where you use an agent that will
look at a set of tools and based on the tools is going to answer your question.
So with this, I hope you can start building your applications today.