In my last post, we explored Natural Language Processing (NLP), the field of AI that helps machines understand human language.
Today, we are taking it one step further with Large Language Models (LLMs),the brains behind tools that can chat, write, generate code, and answer complex questions.
LLMs make building smart, language-aware apps easier than ever, even without deep machine learning expertise 🤩.
💡 What is a Large Language Model (LLM)?
A Large Language Model is an AI system trained on massive amounts of text (books, websites, conversations, code) to understand and generate human-like language.
Most modern LLMs are built on transformer architecture, which makes them exceptionally good at understanding context and producing coherent text.
LLMs can:
- Read and understand natural language (like English, French, or even programming languages)
- Predict what comes next in a sentence or conversation
- Generate text, code, and summaries
- Translate between languages
- Summarize long documents
- Answer questions and assist with research
📌 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, and more, 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. Usage
Once trained, you can prompt the model with an input, and it will generate a coherent response based on everything it has learned.
📌 Types of Large Language Models (LLMs)
LLMs differ in how they are built, what they are trained on, and how they are used.
- General-Purpose – The do many things but need well-crafted prompts (GPT-3, LLaMA, Mistral).
- Instruction-Tuned – They follow natural instructions better (GPT-4, Claude, Gemini).
- Open-Source – You can self-host, customize, and control (LLaMA, Mistral, Falcon, BLOOM).
- Proprietary – You can only access via API because they are fully managed (GPT-4, Claude, Gemini).
- Domain-Specific – Fine-tuned for fields like law, medicine, or coding (Code LLaMA, StarCoder).
- Multilingual – They work across many languages (BLOOM, XLM-R, mGPT).
📌 Where LLMs Are Used
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 & Question-Answer 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. Prompting is the most basic and universal way to use them.
Here are the main approaches developers and teams use 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 information before answering.
Tools: LangChain, LlamaIndex, Haystack
Use case: Building smart document assistants, internal search tools, etc.
📌 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
| Goal Type | Recommended Approach | Example Model |
|---|---|---|
| Simple tasks (chat, Q&A) | Prompting + API | GPT-4, Claude, Gemini |
| Domain-specific outputs | Prompt Engineering or RAG | GPT-4, Cohere Command-R |
| Full control + offline use | Fine-tune open-source models | Mistral, LLaMA, BLOOM |
| Code generation | Use code-focused LLMs | Code LLaMA, StarCoder |
🔸 3. Pick Based on Resources
- Low Resources: Use hosted APIs (OpenAI, Claude, etc)
- Medium Resources: Use Hugging Face models locally
- High on Resources: Fine-tune open-source models with GPUs
🔸 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
- Developer with no ML experience: Prompting, RAG
-
Developer with ML/AI experience: Fine-tuning, Evaluation
Tips:
- Start simple with prompting and hosted APIs.
- Move to RAG or fine-tuning when your app needs domain-specific behavior or more control.
📌 Why LLMs Matter for Developers
LLMs let you add intelligence to your apps without training your own model.
Instead of writing hundreds of rules, you can simply describe what you want in plain English and let the model handle the complexity.
📌 How to Use LLMs in Your Apps
🔸 1. Choose Your LLM Provider - OpenAI API, Anthropic Claude API, Google Gemini API, Hugging Face Inference API
🔸 2. Call the Model from Your Code - An example using OpenAI’s API in Python:
from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY")
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Write a 3-sentence summary of climate change."}
]
)
print(response.choices[0].message["content"])
🔸 3. Give Good Prompts (Prompt Engineering) - The way you ask matters.
- Be specific:
Summarize this text in bullet points. - Give examples:
Here’s a format I want: [Example] - Set a role:
You are a senior software engineer who chooses simplicity over complexity.
🔸 4. Add App Logic Around the Model - LLMs aren’t apps on their own. You wrap them in code.
- Store user queries and responses in a database
- Use NLP to pre-process input (e.g., remove noise, detect intent)
- Chain multiple model calls to complete complex tasks
Summary
LLMs are a game-changer for developers.
They let you build apps that can understand, generate, and interact with language without starting from scratch.
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!!!
Top comments (2)
this is extremely impressive, honestly the structure makes it way easier for me to follow along. you think there’ll ever be a moment we look back and feel like this was the starting point for everyday AI devs
Thank you so much for the kind words, that means a lot 🙌
I am so glad the structure made it easier to follow.
And yes, I genuinely believe we are in that
starting pointphase. Just like how web development had its defining moments, I think this era is where everyday developers begin to shape what AI can do in real-world apps. It's exciting to think that soon we will look back and say