A Beginner’s Guide to LLMs: How to Use Language Models to Build Smart Apps

In my last post, we explored Natural Language Processing (NLP), the field of AI that helps machines understand human language.

A powerful way to explore AI innovations today is through Large Language Models (LLMs), which is the brains behind tools that can chat, write, generate code, and answer questions. LLMs make it easier than ever to build smart, language-aware apps, even without deep machine learning expertise. LLMs have taken NLP to the next level, making language understanding more powerful, accessible, and scalable.

What is a Large Language Model (LLM)?

A Large Language Model (LLM) is an AI system trained on vast amounts of data such as textbooks, websites, code, and conversations, to understand and generate human-like language. It is built on transformer architecture and power many of today’s smartest AI tools.

LLMs can:

• Read and understand natural language (like English, French, or even programming languages)
• Predict what comes next in a sentence or conversation
• Generate responses that are context-aware and coherent
• Translate between languages
• Summarize long documents
• Write articles, emails, or code from a simple prompt

💡 How Do Large Language Models (LLMs) Work?

At the core, LLMs predict the next word in a sentence based on the context of the words before it. This simple idea (next-word-prediction) is what allows them to write emails, answer questions, generate code, and more.

🔸 1. Training on Massive Text Data
LLMs are trained on huge datasets like books, websites, conversations, code, etc., to learn patterns in language. This helps them understand:

• Grammar and syntax
• Facts and world knowledge
• How humans typically phrase things

🔸 2. Tokenization
Before feeding text into the model, it is broken down into smaller pieces called tokens (words or word parts).
For example:
"I love coding" -> ["I", "love", "coding"]

🔸 3. Embeddings
Each token is converted into a vector (a list of numbers) that captures its meaning in context. These vectors are what the model works with.

🔸 4. Transformer Architecture
This is the model architecture that powers LLMs. It uses something called attention to focus on the most relevant words in a sentence.

🔸 5. Next-Word Prediction
During training, the model learns to guess the next word in a sentence:

Input: `The cat sat on the…`
Output: `mat`

By doing this billions of times, it becomes very good at understanding and generating human-like language.

🔸 6. Inference (Usage)
Once trained, you can prompt the model with an input, and it will generate a coherent response based on everything it has learned.

LLMs learn language patterns from massive data using transformers. When you give them a prompt, they predict the most likely next word(s) and repeat until a full response is formed.

💡 Types of Large Language Models (LLMs)

Not all LLMs are created equal. They differ in how they are built, what they are trained on, and how they are used.

1. General-Purpose vs Instruction-Tuned LLMs

General-Purpose LLMs
These models are trained to predict the next word in a sentence. They can do many tasks but need well-crafted prompts to behave correctly.Examples are GPT-3, LLaMA, Mistral

Instruction-Tuned LLMs
These are LLMs fine-tuned to follow instructions directly (e.g., "Summarize this paragraph", "Explain like I am 5"). They respond more helpfully out of the box. Examples are GPT-3.5, GPT-4, Claude, Gemini

2. Open-Source vs Proprietary LLMs

Open-Source LLMs
These are free to use, modify, and self-host. Great for privacy, customization, or budget-conscious developers. Examples are LLaMA by Meta, Mistral, Falcon, GPT-J

Proprietary/Commercial LLMs
These are built and maintained by companies (like OpenAI and Google). You access them via APIs. Examples are GPT-4 by OpenAI, Claude by Anthropic, Gemini by Google, Cohere Command-R

3. Domain-Specific LLMs

These are models fine-tuned on specific types of data for focused tasks:

• 📚 Legal or medical LLMs: LLMs trained on case law or clinical data
• 🤖 Code-focused LLMs: LLMs fine-tuned on programming languages. Examples are Codex (used in GitHub Copilot), Code LLaMA, StarCoder

4. Multilingual LLMs

These models are trained on multiple languages to handle global tasks:

• XLM-RoBERTa
• mGPT (multilingual GPT)
• BLOOM (can generate text in 40+ languages)

💡 Examples of Popular LLMs

Here are some of the most well-known LLMs today:

💡 Real-World Use Cases of LLMs

LLMs are powering real-world applications across industries. Here are just a few:

1. Chatbots & Virtual Assistants
LLMs enable natural conversations - customer service bots, AI therapists, HR assistants.
💬 Example: ChatGPT, Claude, Replika

2. Content Generation
LLMs help create quality text with little human effort - blog posts.
📝 Example: Jasper AI, Notion AI, Copy.ai

3. Knowledge Assistants & Q&A Systems
LLMs are used in education, legal, and healthcare to answer complex domain-specific questions.
📚 Example: AI tutors, Legal search bots, Medical chatbots

4. Text Summarization & Report Generation
Used in journalism, legal, and finance to turn long documents into clear summaries.
📄 Example: Tools like Scribe, SummarizeBot

5. Code Generation & Debugging
LLMs fine-tuned on code can generate, explain, and fix programming tasks.
👨‍💻 Example: GitHub Copilot, Amazon CodeWhisperer

💡 Approaches to Use LLMs Effectively

You don’t always need to train your own model to benefit from LLMs. Here are practical ways developers and teams use them today:
🔸 1. Prompt Engineering
This is the fastest and easiest approach to get the best from LLMs. It is the process of crafting effective input (prompts) to guide the model’s output. This is the most common and beginner-friendly approach.

• Example: Summarize this paragraph in 3 bullet points…, Translate this into French…

🔸 2. Function Calling/Tool Use
Let the LLM call specific tools (e.g., weather APIs, database queries) when needed. Most modern APIs support this (like OpenAI’s functions or tools).

• Example: An AI chatbot that retrieves live stock prices or booking details.

🔸 3. Fine-Tuning (Advanced)
This involves training an existing model on your own custom dataset to specialize it for your domain.

• Use this approach if the model isn’t performing well on specific tasks, or you need domain-specific responses (e.g., medical, legal, or company data).

🔸 4. Retrieval-Augmented Generation (RAG)
Combine LLMs with your own knowledge base (e.g., company docs, PDF files). Instead of fine-tuning, the model retrieves relevant info before answering.

• Tools: LangChain, LlamaIndex, Haystack

• Use case: Building smart document assistants, internal search tools, etc.

🔸 5. Embedding + Vector Search
Convert user queries and documents into embeddings (vector form), then find the most relevant match.

• Use case: Semantic search, personalized recommendations.

🔸 6. Multi-Agent Systems
Use multiple LLMs (or roles) that collaborate to solve a task like one agent plans, another executes, and another reviews.

💡 How to Choose the Right LLM and Approach

With so many LLMs and usage methods available, it's easy to feel overwhelmed. Here’s a simple way to decide what is best for your use case:

🔸 1. Choose Based On Your Goal: What do you want the model to do?
Examples:

• Summarize tasks: Use models tuned for summarization (OpenAI GPT-4-turbo, Anthropic Claude 3)
• Build a chatbot: Use general-purpose conversational models (GPT-4/GPT-4-turbo, Gemini 1.5 Pro)
• Extract structured data: Use LLMs with structured prompting or Retrieval-Augmented Generation (RAG) (GPT-4-turbo with Function Calling, Command R+ by Cohere)

🔸 2. Choose Based on Complexity

🔸 3. Pick Based on Resources

🔸 4. Consider Data Privacy & Ownership

• Working with sensitive/private data?: Use open-source LLMs locally.
• General-purpose tasks?: Hosted APIs are fast and convenient.

🔸 5. Consider Your Skill Level

• Start simple with prompting and hosted APIs.
• Move to RAG or fine-tuning when your app needs domain-specific behavior or more control.

Summary

This post introduces Large Language Models (LLMs) as powerful tools for building smart, language-aware applications. It covers what LLMs are, how they evolved from NLP, the different types available, and practical approaches like prompting, fine-tuning, and RAG.

Happy coding!!!