Many developers are excited about Large Language Models (LLMs), like ChatGPT, and want to start building AI applications. If you’re new to AI, it might seem complicated at first. What is behind the scene? I’ll help you get started. I’ll share my own experiences and explain the main parts of an LLM application using simple terms. We’ll also look at real-world business examples to show clearly why each part is important and how they all connect.

1. User Query: The Entry Point

Every LLM application starts when a user types a query. A user query can be structured or unstructured. Structured queries are clear and organized, like JSON-formatted requests. Unstructured queries are everyday questions, such as “Do you have shoes under $100?” or “What’s today’s weather?”.

One important task is deciding how to handle queries that aren’t helpful or relevant. For example, if you’re building a chatbot for your online store, you might not want it to answer questions about your competitor’s products or locations. Your chatbot should act as a polite gatekeeper, deciding what questions to answer and what to skip.

2. Prompt Engineering: Making Queries Clear

Prompt engineering helps the LLM clearly understand what the user wants. Good prompts lead to better answers. There are two common ways to structure prompts. The first is using structured JSON, which is good for applications that need to connect easily with databases or APIs. For example:

{
  "intent": "product_search",
  "product_type": "shoes",
  "max_price": "100"
}

The second way is using simple text prompts, ideal for conversational applications, like “List shoes under $100.”

A good prompt is clear, specific, and provides enough context to guide the response while allowing flexibility for creativity or problem-solving. It should include key details like the goal, format, tone, and any constraints, making it easy for the person (or AI) to understand exactly what is needed. Instead of asking something vague like “Tell me about data engineering,” a better prompt would be “Explain data engineering in simple terms with examples of real-world use cases.” This makes the request more focused and ensures a more relevant, high-quality response.

Prompt templates make working with AI easier by giving structure to your requests, so you don’t have to start from scratch every time. They help keep responses clear, consistent, and relevant, which is especially useful for things like eCommerce, customer support, or content creation. Instead of struggling to get the right answer, a well-designed template guides the AI to produce exactly what you need. For example, if you’re running an online store, you could use a simple template like “Write a product description for {product_name}, highlighting {features} and explaining how it benefits {target_audience}.” This way, you can quickly generate engaging product descriptions without spending extra time crafting each one manually. It’s an easy way to make AI work smarter for you.

Another essential part of prompt engineering is the system message. A system message tells the LLM how to behave, what tone to use, and what information to provide. For example: “You are a helpful shopping assistant. Provide structured JSON responses about our products only.”

Clear prompts and system messages help ensure your application responds consistently and accurately.

3. RAG Layer and Vector Database: Finding Relevant Information

The Retrieval-Augmented Generation (RAG) layer helps the LLM find the right information quickly by using a special type of database called a vector database. When a user query arrives, the RAG layer converts the query into a “vector” (think of it as a digital fingerprint). It then searches the vector database for information closely matching this vector. This information is added to the user’s query, forming an augmented prompt that helps the LLM produce accurate responses.

For instance, if someone asks about waterproof jackets, the RAG layer quickly finds product details and inventory data. It then combines this information with the user’s question and creates an augmented prompt for the LLM to answer accurately.

Think of the RAG layer like quickly searching Wikipedia to create a detailed response.

4. LLM Model: The Brain of the Application

The Large Language Model itself generates the answers. It uses the prompts and additional context provided by the RAG layer to create accurate responses. Choosing the right LLM model is important. You can select open-source models (like Llama) or proprietary models (like GPT-4).

Consider the following factors when choosing an LLM:

• Cost: Proprietary models are easy to set up but can be expensive. Open-source models may be cheaper but often need more setup effort.
• Speed and Reliability: Proprietary models are typically faster and more reliable.
• Privacy: Open-source models offer better data control, essential for handling sensitive information.

You can also adjust how the LLM behaves by changing parameters like temperature, which controls how creative or predictable responses are, and Top-p or Top-k sampling, which help ensure responses are relevant.

If the LLM requires expertise in areas like medical diagnosis, legal documents, or scientific research, fine-tuning helps the model understand technical terms and industry-specific language. It is another way to customize the model. This means training your LLM specifically for your business. Fine-tuning also helps to reducing token cost and latency because we do not need to feed complex and lengthy augmented prompts every time. However, fine-tuning is not always necessary, if your use case can be handled with prompt engineering, retrieval-augmented generation (RAG), or API parameter tuning.

5. Output: Delivering the Answer

The LLM output can be structured or unstructured, depending on your needs. Structured outputs (like JSON) are great for integrating with other software, while unstructured outputs work well for chat applications.

Here’s an example of a structured JSON output:

{
  "product_name": "RainMaster Waterproof Jacket",
  "price": "$89.99",
  "inventory_status": "In Stock"
}

Summary: Understanding LLM Basics

Understanding these core components is just the beginning of your journey with LLMs. Clear user queries, effective prompt engineering (including system messages), fast retrieval of relevant information, smart choice of LLM models, and structured outputs are foundational. Master these, and you’ll build reliable and user-friendly AI applications.