How do I build my first LLM application?

Start by choosing an LLM API (OpenAI, Anthropic, or local via Ollama), create a simple backend that calls the API, add conversation memory to store chat history, and build a frontend interface. Use provider-agnostic code so you can switch models later. Add error handling and cost tracking from the start.

What programming language should I use for LLM applications?

Python and JavaScript/Node.js are the most popular choices. Python has excellent LLM libraries like LangChain and direct API clients. Node.js works well if you are already building web applications. Both languages have good OpenAI and Anthropic SDKs. Choose based on your existing stack.

How do I add conversation memory to my LLM application?

Store chat history in a database (PostgreSQL, MongoDB) or session storage. Send the full conversation history with each API request. When the context window fills up, summarize older messages or truncate them. For production apps, use vector databases for long-term memory retrieval.

How much does it cost to build an LLM application?

API costs vary by model: GPT-4o costs $2.50-$15 per million tokens, Claude 3.5 Sonnet costs $3-$15 per million tokens. A simple chat application might cost $10-50/month for moderate usage. Local models via Ollama are free after hardware costs. Always track token usage to control costs.

What is the difference between a chatbot and an LLM application?

A chatbot is a simple interface that sends prompts to an LLM. An LLM application includes conversation memory, error handling, user management, tool integration, and production features like monitoring and cost tracking. LLM applications are built for real users, not just demos.

Should I use LangChain or build from scratch?

Start from scratch to understand the fundamentals. Once you understand how LLM APIs work, LangChain can save time for complex features like RAG, agents, and multi-step workflows. For simple chat applications, raw API calls are often simpler.

How do I handle LLM API errors and rate limits?

Implement retry logic with exponential backoff for transient errors. Set timeouts for API calls. Handle rate limit errors by queuing requests or using multiple API keys. Log all errors for debugging. Show user-friendly error messages in your frontend.

Can I run LLM applications locally without cloud APIs?

Yes. Use Ollama to run open source models like Llama, Mistral, or DeepSeek locally. They provide OpenAI-compatible APIs, so your code works the same. Local models are free but require sufficient RAM (8GB+ for 7B models). See our guide on [running LLMs locally](/running-llms-locally/).

How to Build an LLM Application From Scratch

You want to build an LLM application. Maybe a chatbot for customer support. Maybe a coding assistant. Maybe something that helps users search through documents. But where do you start?

Most tutorials show you how to call an API and get a response. That is not an application. That is a demo. Real applications need conversation memory, error handling, user management, and production features.

This guide walks you through building a complete LLM application from scratch. No frameworks required. Just the fundamentals you need to understand before using tools like LangChain or LlamaIndex.

TL;DR: Build your first LLM application in four steps: choose an LLM API, create a backend that handles conversations, add a frontend, and deploy. Use provider-agnostic code so you can switch models. Add error handling, memory, and cost tracking from day one.

What You Will Build

We will build a simple but complete chat application that:

Connects to an LLM API (OpenAI, Anthropic, or local)
Maintains conversation history
Handles errors gracefully
Tracks costs
Works in production

This is not a toy. This is the foundation you can extend into any LLM application you need.

Understanding LLM Application Architecture

Before writing code, understand the architecture. Every LLM application follows the same pattern:

flowchart TB
    subgraph Frontend["Frontend Layer"]
        UI[fa:fa-window-maximize User Interface]
    end
    
    subgraph Backend["Backend Layer"]
        API[fa:fa-server API Server]
        Memory[fa:fa-database Conversation Memory]
        Router[fa:fa-random Request Router]
    end
    
    subgraph LLM["LLM Provider"]
        Adapter[fa:fa-plug LLM Adapter]
        Provider[fa:fa-cloud LLM API]
    end
    
    UI -->|User Message| API
    API -->|Store| Memory
    API -->|Get History| Memory
    API -->|Send Request| Router
    Router -->|Route| Adapter
    Adapter -->|API Call| Provider
    Provider -->|Response| Adapter
    Adapter -->|Response| Router
    Router -->|Response| API
    API -->|Response| Memory
    API -->|Response| UI
    
    style UI fill:#e3f2fd,stroke:#1565c0,stroke-width:2px
    style API fill:#fff3e0,stroke:#e65100,stroke-width:2px
    style Memory fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px
    style Adapter fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style Provider fill:#fce4ec,stroke:#c2185b,stroke-width:2px

Frontend: The user interface where users type messages and see responses.

Backend API: Handles requests, manages conversation memory, and routes to the LLM.

LLM Adapter: Provider-agnostic layer that lets you switch between OpenAI, Claude, local models, etc.

LLM Provider: The actual API or local model that generates responses.

Memory: Stores conversation history so the LLM has context from previous messages.

This architecture separates concerns. You can swap the frontend, change LLM providers, or add features without rewriting everything.

Step 1: Choose Your LLM Provider

You have three main options:

Option 1: OpenAI API

Pros: Best documentation, most tutorials, reliable, fast

Cons: Costs money, data sent to external servers

Best for: Getting started quickly, production applications

# OpenAI API example
from openai import OpenAI

client = OpenAI(api_key="your-key")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello"}]
)

Option 2: Anthropic Claude API

Pros: Excellent quality, long context windows, good for coding

Cons: Costs money, newer than OpenAI

Best for: Applications needing long context or coding assistance

# Anthropic API example
from anthropic import Anthropic

client = Anthropic(api_key="your-key")
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    messages=[{"role": "user", "content": "Hello"}]
)

Option 3: Local Models via Ollama

Pros: Free, private, works offline

Cons: Requires hardware, slower than cloud APIs

Best for: Privacy-sensitive applications, cost-sensitive use cases

See our guide on running LLMs locally for setup instructions.

# Ollama API (OpenAI-compatible)
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # Not used but required
)
response = client.chat.completions.create(
    model="llama3.3",
    messages=[{"role": "user", "content": "Hello"}]
)

My Recommendation

Start with OpenAI API for the best developer experience. Once you understand the patterns, you can switch to Claude for better quality or local models for privacy. The code we write will work with all three.

Step 2: Build the Backend

We will build a Python backend using FastAPI. If you prefer Node.js, the patterns are the same.

Project Setup

mkdir llm-app
cd llm-app
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install fastapi uvicorn openai python-dotenv

Create a .env file:

OPENAI_API_KEY=your-key-here

Create the LLM Adapter

First, build a provider-agnostic adapter. This lets you switch between providers without changing your application code:

# llm_adapter.py
from abc import ABC, abstractmethod
from typing import List, Dict
from openai import OpenAI
import os

# ABC stands for Abstract Base Class - it prevents you from creating
# LLMAdapter directly. You must create a concrete implementation like OpenAIAdapter.
class LLMAdapter(ABC):
    @abstractmethod
    def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
        pass

class OpenAIAdapter(LLMAdapter):
    def __init__(self, api_key: str = None):
        self.client = OpenAI(api_key=api_key or os.getenv("OPENAI_API_KEY"))
        self.model = "gpt-4o"
    
    def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=kwargs.get("temperature", 0.7),
            max_tokens=kwargs.get("max_tokens", 1000)
        )
        return response.choices[0].message.content

class AnthropicAdapter(LLMAdapter):
    def __init__(self, api_key: str = None):
        from anthropic import Anthropic
        self.client = Anthropic(api_key=api_key or os.getenv("ANTHROPIC_API_KEY"))
        self.model = "claude-3-5-sonnet-20241022"
    
    def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
        response = self.client.messages.create(
            model=self.model,
            messages=messages,
            temperature=kwargs.get("temperature", 0.7),
            max_tokens=kwargs.get("max_tokens", 1000)
        )
        return response.content[0].text

class OllamaAdapter(LLMAdapter):
    def __init__(self, base_url: str = "http://localhost:11434/v1"):
        self.client = OpenAI(
            base_url=base_url,
            api_key="ollama"
        )
        self.model = "llama3.3"
    
    def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=kwargs.get("temperature", 0.7),
            max_tokens=kwargs.get("max_tokens", 1000)
        )
        return response.choices[0].message.content

This adapter pattern is crucial. Your application code never directly calls OpenAI or Claude. It calls the adapter, which you can swap out.

Create the API Server

# main.py
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Dict, Optional
from llm_adapter import OpenAIAdapter, LLMAdapter
import os

app = FastAPI()

# Enable CORS for frontend
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify your frontend domain
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Initialize LLM adapter
llm: LLMAdapter = OpenAIAdapter()

# In-memory storage (use a database in production)
conversations: Dict[str, List[Dict[str, str]]] = {}

class ChatRequest(BaseModel):
    message: str
    conversation_id: Optional[str] = None
    temperature: Optional[float] = 0.7
    max_tokens: Optional[int] = 1000

class ChatResponse(BaseModel):
    response: str
    conversation_id: str
    tokens_used: Optional[int] = None

@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    try:
        # Get or create conversation
        conversation_id = request.conversation_id or f"conv_{len(conversations)}"
        
        if conversation_id not in conversations:
            conversations[conversation_id] = [
                {"role": "system", "content": "You are a helpful assistant."}
            ]
        
        # Add user message
        conversations[conversation_id].append({
            "role": "user",
            "content": request.message
        })
        
        # Get LLM response
        response_text = llm.chat(
            messages=conversations[conversation_id],
            temperature=request.temperature,
            max_tokens=request.max_tokens
        )
        
        # Add assistant response to history
        conversations[conversation_id].append({
            "role": "assistant",
            "content": response_text
        })
        
        return ChatResponse(
            response=response_text,
            conversation_id=conversation_id
        )
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health():
    return {"status": "ok"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Run the server:

python main.py

You now have a working LLM API. Test it:

curl -X POST http://localhost:8000/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello, how are you?"}'

Add Error Handling

LLM APIs can fail. Add proper error handling:

import time
from openai import RateLimitError, APIError

def chat_with_retry(self, messages: List[Dict[str, str]], max_retries: int = 3, **kwargs) -> str:
    for attempt in range(max_retries):
        try:
            return self.chat(messages, **kwargs)
        except RateLimitError:
            wait_time = 2 ** attempt  # Exponential backoff
            time.sleep(wait_time)
        except APIError as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(1)
    raise Exception("Failed after retries")

Add Cost Tracking

Track token usage to control costs:

class OpenAIAdapter(LLMAdapter):
    def __init__(self, api_key: str = None):
        self.client = OpenAI(api_key=api_key or os.getenv("OPENAI_API_KEY"))
        self.model = "gpt-4o"
        self.total_tokens = 0
        self.total_cost = 0.0
    
    def chat(self, messages: List[Dict[str, str]], **kwargs) -> tuple[str, int, float]:
        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=kwargs.get("temperature", 0.7),
            max_tokens=kwargs.get("max_tokens", 1000)
        )
        
        tokens_used = response.usage.total_tokens
        cost = self._calculate_cost(tokens_used, response.usage.prompt_tokens)
        
        self.total_tokens += tokens_used
        self.total_cost += cost
        
        return response.choices[0].message.content, tokens_used, cost
    
    def _calculate_cost(self, total_tokens: int, prompt_tokens: int) -> float:
        # GPT-4o pricing (as of 2026)
        input_cost_per_1k = 2.50 / 1000
        output_cost_per_1k = 10.00 / 1000
        
        completion_tokens = total_tokens - prompt_tokens
        cost = (prompt_tokens * input_cost_per_1k) + (completion_tokens * output_cost_per_1k)
        return cost

Step 3: Add Conversation Memory

The in-memory storage above works for demos. For production, use a database:

# database.py
import sqlite3
from typing import List, Dict
import json

class ConversationDB:
    def __init__(self, db_path: str = "conversations.db"):
        self.conn = sqlite3.connect(db_path)
        self._create_tables()
    
    def _create_tables(self):
        self.conn.execute("""
            CREATE TABLE IF NOT EXISTS conversations (
                id TEXT PRIMARY KEY,
                messages TEXT,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        """)
        self.conn.commit()
    
    def get_messages(self, conversation_id: str) -> List[Dict[str, str]]:
        cursor = self.conn.execute(
            "SELECT messages FROM conversations WHERE id = ?",
            (conversation_id,)
        )
        row = cursor.fetchone()
        if row:
            return json.loads(row[0])
        return [{"role": "system", "content": "You are a helpful assistant."}]
    
    def save_messages(self, conversation_id: str, messages: List[Dict[str, str]]):
        messages_json = json.dumps(messages)
        self.conn.execute("""
            INSERT OR REPLACE INTO conversations (id, messages, updated_at)
            VALUES (?, ?, CURRENT_TIMESTAMP)
        """, (conversation_id, messages_json))
        self.conn.commit()
    
    def delete_conversation(self, conversation_id: str):
        self.conn.execute("DELETE FROM conversations WHERE id = ?", (conversation_id,))
        self.conn.commit()

Update your API to use the database:

from database import ConversationDB

db = ConversationDB()

@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    conversation_id = request.conversation_id or f"conv_{int(time.time())}"
    
    # Get existing messages
    messages = db.get_messages(conversation_id)
    
    # Add user message
    messages.append({"role": "user", "content": request.message})
    
    # Get LLM response
    response_text, tokens_used, cost = llm.chat(messages, **request.dict())
    
    # Add assistant response
    messages.append({"role": "assistant", "content": response_text})
    
    # Save to database
    db.save_messages(conversation_id, messages)
    
    return ChatResponse(
        response=response_text,
        conversation_id=conversation_id,
        tokens_used=tokens_used
    )

Handle Context Window Limits

LLMs have token limits. When conversations get long, you need to manage context:

def truncate_messages(messages: List[Dict[str, str]], max_tokens: int = 4000) -> List[Dict[str, str]]:
    """Keep only recent messages that fit within token limit."""
    # Simple approach: keep system message + last N messages
    if len(messages) <= 2:
        return messages
    
    system_message = messages[0]
    recent_messages = messages[-10:]  # Keep last 10 messages
    
    # In production, use tiktoken to count actual tokens
    return [system_message] + recent_messages

For production, use a proper token counter:

import tiktoken

def count_tokens(messages: List[Dict[str, str]], model: str = "gpt-4o") -> int:
    encoding = tiktoken.encoding_for_model(model)
    tokens = 0
    for message in messages:
        tokens += 4  # Message overhead
        tokens += len(encoding.encode(message["content"]))
    return tokens

Step 4: Build the Frontend

Create a simple HTML frontend:

<!-- index.html -->
<!DOCTYPE html>
<html>
<head>
    <title>LLM Chat Application</title>
    <style>
        body {
            font-family: system-ui, -apple-system, sans-serif;
            max-width: 800px;
            margin: 0 auto;
            padding: 20px;
        }
        #chat {
            border: 1px solid #ddd;
            border-radius: 8px;
            padding: 20px;
            height: 400px;
            overflow-y: auto;
            margin-bottom: 20px;
        }
        .message {
            margin-bottom: 15px;
        }
        .user {
            text-align: right;
        }
        .assistant {
            text-align: left;
        }
        .message-content {
            display: inline-block;
            padding: 10px 15px;
            border-radius: 8px;
            max-width: 70%;
        }
        .user .message-content {
            background: #007bff;
            color: white;
        }
        .assistant .message-content {
            background: #f0f0f0;
            color: #333;
        }
        #input-form {
            display: flex;
            gap: 10px;
        }
        #message-input {
            flex: 1;
            padding: 10px;
            border: 1px solid #ddd;
            border-radius: 4px;
        }
        button {
            padding: 10px 20px;
            background: #007bff;
            color: white;
            border: none;
            border-radius: 4px;
            cursor: pointer;
        }
        button:disabled {
            background: #ccc;
            cursor: not-allowed;
        }
        .loading {
            opacity: 0.6;
        }
    </style>
</head>
<body>
    <h1>LLM Chat Application</h1>
    <div id="chat"></div>
    <form id="input-form">
        <input type="text" id="message-input" placeholder="Type your message..." />
        <button type="submit" id="send-button">Send</button>
    </form>

    <script>
        const API_URL = 'http://localhost:8000';
        let conversationId = null;

        const chatDiv = document.getElementById('chat');
        const form = document.getElementById('input-form');
        const input = document.getElementById('message-input');
        const button = document.getElementById('send-button');

        function addMessage(role, content) {
            const messageDiv = document.createElement('div');
            messageDiv.className = `message ${role}`;
            
            const contentDiv = document.createElement('div');
            contentDiv.className = 'message-content';
            contentDiv.textContent = content;
            
            messageDiv.appendChild(contentDiv);
            chatDiv.appendChild(messageDiv);
            chatDiv.scrollTop = chatDiv.scrollHeight;
        }

        async function sendMessage(message) {
            addMessage('user', message);
            
            button.disabled = true;
            input.disabled = true;
            chatDiv.classList.add('loading');

            try {
                const response = await fetch(`${API_URL}/chat`, {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json',
                    },
                    body: JSON.stringify({
                        message: message,
                        conversation_id: conversationId
                    })
                });

                if (!response.ok) {
                    throw new Error('Failed to get response');
                }

                const data = await response.json();
                conversationId = data.conversation_id;
                addMessage('assistant', data.response);
            } catch (error) {
                addMessage('assistant', `Error: ${error.message}`);
            } finally {
                button.disabled = false;
                input.disabled = false;
                chatDiv.classList.remove('loading');
                input.focus();
            }
        }

        form.addEventListener('submit', (e) => {
            e.preventDefault();
            const message = input.value.trim();
            if (message) {
                input.value = '';
                sendMessage(message);
            }
        });
    </script>
</body>
</html>

Open index.html in a browser. You now have a working LLM chat application.

Step 5: Add Production Features

Your application works, but it is not production-ready. Add these features:

Rate Limiting

Prevent abuse:

from slowapi import Limiter, _rate_limit_exceeded_handler
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded

limiter = Limiter(key_func=get_remote_address)
app.state.limiter = limiter
app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)

@app.post("/chat")
@limiter.limit("10/minute")
async def chat(request: Request, chat_request: ChatRequest):
    # ... existing code

Input Validation

Sanitize user input:

from pydantic import validator

class ChatRequest(BaseModel):
    message: str
    conversation_id: Optional[str] = None
    
    @validator('message')
    def validate_message(cls, v):
        if len(v) > 10000:
            raise ValueError('Message too long')
        if not v.strip():
            raise ValueError('Message cannot be empty')
        return v.strip()

Logging

Log all requests for debugging:

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@app.post("/chat")
async def chat(request: ChatRequest):
    logger.info(f"Chat request: conversation_id={request.conversation_id}, message_length={len(request.message)}")
    # ... rest of code

Health Checks

Monitor your application:

@app.get("/health")
async def health():
    try:
        # Test LLM connection
        test_response = llm.chat([{"role": "user", "content": "test"}])
        return {
            "status": "healthy",
            "llm": "connected",
            "database": "connected"
        }
    except Exception as e:
        return {
            "status": "unhealthy",
            "error": str(e)
        }, 503

Common Patterns and Best Practices

Pattern 1: System Prompts

Use system prompts to define behavior:

SYSTEM_PROMPTS = {
    "default": "You are a helpful assistant.",
    "coding": "You are a senior software engineer. Provide concise, practical code examples.",
    "support": "You are a customer support agent. Be empathetic and solution-focused."
}

def get_messages(conversation_id: str, prompt_type: str = "default"):
    messages = db.get_messages(conversation_id)
    if not messages or messages[0]["role"] != "system":
        messages.insert(0, {"role": "system", "content": SYSTEM_PROMPTS[prompt_type]})
    return messages

Pattern 2: Streaming Responses

For better UX, stream responses as they generate:

from fastapi.responses import StreamingResponse

@app.post("/chat/stream")
async def chat_stream(request: ChatRequest):
    async def generate():
        # Get streaming response from LLM
        stream = llm.client.chat.completions.create(
            model=llm.model,
            messages=messages,
            stream=True
        )
        
        for chunk in stream:
            if chunk.choices[0].delta.content:
                yield f"data: {chunk.choices[0].delta.content}\n\n"
    
    return StreamingResponse(generate(), media_type="text/event-stream")

Pattern 3: Function Calling

Let the LLM call your functions:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"}
                },
                "required": ["city"]
            }
        }
    }
]

response = llm.client.chat.completions.create(
    model=llm.model,
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

See our guide on building AI agents for more on function calling.

Deployment

Deploy to Railway

Create Procfile:

web: uvicorn main:app --host 0.0.0.0 --port $PORT

Create requirements.txt:

fastapi
uvicorn
openai
python-dotenv

Push to GitHub and connect Railway

Deploy to Render

Similar to Railway. Render automatically detects FastAPI applications.

Deploy with Docker

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Testing Your Application

Write tests before shipping:

# test_chat.py
import pytest
from fastapi.testclient import TestClient
from main import app

client = TestClient(app)

def test_chat_endpoint():
    response = client.post("/chat", json={"message": "Hello"})
    assert response.status_code == 200
    assert "response" in response.json()
    assert "conversation_id" in response.json()

def test_conversation_memory():
    # Send first message
    response1 = client.post("/chat", json={"message": "My name is Ajit"})
    conversation_id = response1.json()["conversation_id"]
    
    # Send second message
    response2 = client.post("/chat", json={
        "message": "What is my name?",
        "conversation_id": conversation_id
    })
    
    assert "Ajit" in response2.json()["response"]

Cost Optimization

LLM APIs charge per token. Optimize costs:

Use smaller models when possible: GPT-4o mini is 10x cheaper than GPT-4o
Cache common responses: Store frequently asked questions
Limit max_tokens: Prevent runaway generations
Summarize long conversations: Compress old messages instead of sending full history
Use local models: Ollama is free after hardware costs

Next Steps

You now have a working LLM application. Here is what to build next:

Add RAG (Retrieval Augmented Generation): Let the LLM search your documents
Build AI agents: Add tool calling for autonomous actions
Add user authentication: Multi-user support with proper security
Implement fine-tuning: Train custom models for your use case
Add monitoring: Track usage, errors, and costs in production

For deeper dives, see:

How LLMs Generate Text - Understand the internals
Building AI Agents - Add autonomous capabilities
Running LLMs Locally - Privacy and cost savings
Context Engineering - Optimize what you send to LLMs

Key Takeaways

Start simple: Build a basic chat application first. Add complexity only when needed.
Use provider-agnostic code: Abstract LLM calls so you can switch providers easily.
Handle errors: LLM APIs fail. Add retries, timeouts, and graceful error handling.
Track costs: Token usage adds up. Monitor costs from day one.
Test thoroughly: LLMs are non-deterministic. Test with real scenarios before shipping.
Plan for scale: Use databases for memory, add rate limiting, and monitor performance.

Building LLM applications is not magic. It is software engineering with a new type of API. Follow these patterns, and you will build applications that work in production.

Related Posts:

How LLMs Generate Text - Understand what happens inside the model
Building AI Agents - Add autonomous capabilities to your application
Running LLMs Locally - Use local models for privacy and cost savings
Context Engineering - Optimize prompts and context windows
LLM Inference Speed Comparison - Benchmark different models

Have questions about building LLM applications? Share your experience in the comments below.

How to Build an LLM Application From Scratch

A practical guide to building production-ready LLM applications from scratch

What You Will Build

Understanding LLM Application Architecture

Step 1: Choose Your LLM Provider

Option 1: OpenAI API

Option 2: Anthropic Claude API

Option 3: Local Models via Ollama

My Recommendation

Step 2: Build the Backend

Project Setup

Create the LLM Adapter

Create the API Server

Add Error Handling

Add Cost Tracking

Step 3: Add Conversation Memory

Handle Context Window Limits

Step 4: Build the Frontend

Step 5: Add Production Features

Rate Limiting

Input Validation

Logging

Health Checks

Common Patterns and Best Practices

Pattern 1: System Prompts

Pattern 2: Streaming Responses

Pattern 3: Function Calling

Deployment

Deploy to Railway

Deploy to Render

Deploy with Docker

Testing Your Application

Cost Optimization

Next Steps

Key Takeaways

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More from Ai

What You Will Build

Understanding LLM Application Architecture

Step 1: Choose Your LLM Provider

Option 1: OpenAI API

Option 2: Anthropic Claude API

Option 3: Local Models via Ollama

My Recommendation

Step 2: Build the Backend

Project Setup

Create the LLM Adapter

Create the API Server

Add Error Handling

Add Cost Tracking

Step 3: Add Conversation Memory

Handle Context Window Limits

Step 4: Build the Frontend

Step 5: Add Production Features

Rate Limiting

Input Validation

Logging

Health Checks

Common Patterns and Best Practices

Pattern 1: System Prompts

Pattern 2: Streaming Responses

Pattern 3: Function Calling

Deployment

Deploy to Railway

Deploy to Render

Deploy with Docker

Testing Your Application

Cost Optimization

Next Steps

Key Takeaways

Subscribe via RSS Feed

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Related Posts

More from Ai