Builder Learning Path

📖 6 min read learningbuilder

1-week hands-on guide for developers building AI applications

Key Takeaways

Learn to integrate Claude, GPT, and Gemini via their APIs
Build RAG systems and agentic workflows from scratch
Covers best practices for production deployment

A practical 1-week path for developers building AI applications. We assume you can code but might be new to LLM APIs.

Time commitment: ~10-15 hours spread across 1-2 weeks
Prerequisites: Familiarity with your language of choice (Python, JavaScript, Go, etc.)

Week 1: Day 1-2 - Choose Your Stack

Step 1: Pick Your API (30 min)

You have three choices:

Claude Opus 4.7 (Anthropic)

Best for: Reasoning, writing, analysis
Context window: 400K tokens (read entire books and codebases)
Cost: $15/$ 75 per 1M input/output tokens
When to use: Complex logic, long documents, nuanced writing

GPT-5.5 (OpenAI)

Best for: Fast, all-around, balanced
Context window: 128K tokens
Cost: $2/$ 8 per 1M input/output tokens
When to use: Speed, cost-efficiency, general tasks

Gemini 3.1 Pro (Google)

Best for: Very long documents, research
Context window: 1M tokens (entire books, codebases, papers)
Cost: $2/$ 12 per 1M tokens (free tier available)
When to use: Document processing, massive context tasks

Recommendation for beginners: Start with Claude Opus or GPT-5.5. Both have free tier + paid options. Claude’s documentation and API are excellent.

Step 2: Set Up Your Environment (30 min)

Python:

pip install anthropic

pip install openai

JavaScript/Node:

npm install @anthropic-ai/sdk
# or
npm install openai

Get an API key:

Claude: console.anthropic.com (free credits, includes Opus 4.7)
GPT-5.5: platform.openai.com (free credits)
Gemini: aistudio.google.com (free tier)

Step 3: Make Your First Call (1 hour)

Python with Claude:

from anthropic import Anthropic

client = Anthropic()
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "What is the capital of France?"}
    ]
)
print(message.content[0].text)

JavaScript with Claude:

const Anthropic = require("@anthropic-ai/sdk");

const client = new Anthropic();
const message = await client.messages.create({
  model: "claude-3-5-sonnet-20241022",
  max_tokens: 1024,
  messages: [
    { role: "user", content: "What is the capital of France?" }
  ]
});
console.log(message.content[0].text);

What’s happening:

You create a client with your API key (read from env)
You send a message (one turn of conversation)
You get back a response

This works. You’ve now built an AI assistant.

Week 1: Day 3 - Build Multi-Turn Conversations

Single messages are limited. Real apps need context. Build a chatbot that remembers previous messages.

Key insight: The API is stateless. You manage the conversation history.

messages = []

def chat(user_input):
    messages.append({"role": "user", "content": user_input})

    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=messages
    )

    assistant_message = response.content[0].text
    messages.append({"role": "assistant", "content": assistant_message})

    return assistant_message

# Usage:
print(chat("What's the capital of France?"))
print(chat("What's the population?"))  # Model remembers France context
print(chat("And its ranking by size?"))  # Can refer back

Important: Each API call includes the full history. So:

First message: 1 message sent, 1 response received
Second message: 3 messages sent (user 1, assistant 1, user 2), 1 response received
Third message: 5 messages sent (all history + user 3), 1 response received

This is why long conversations get expensive. Solution: context windows or summaries (advanced).

Week 1: Day 4 - Add Retrieval (RAG Basics)

Now you need your model to know about your data. Add a vector database.

Problem: LLMs have a training cutoff. Claude Opus 4.7 was trained until April 2024. If you ask about events after that date, it won’t know. Use RAG or web-search tools to cover current information.

Solution: Upload your data. Then:

User asks a question
Search your data for relevant snippets
Add those snippets to the prompt as context
Send to LLM

Simplest RAG Setup: For a complete working implementation (chunking, embedding, retrieval, reranking), see the RAG System Template. It covers the same pattern with production-ready code.

What’s happening:

Your documents get converted to embeddings (numerical representations)
The user’s question gets converted to an embedding
You find documents with similar embeddings
You include those as context in your LLM prompt

Result: Your LLM can now answer questions about your data, even if it wasn’t in its training set.

Production options:

Chroma: Local, simple, great for prototyping
Pinecone: Cloud-hosted, scalable, free tier
Weaviate: Open-source, production-ready

Week 1: Day 5-6 - Add Structured Output

Your app needs to extract data, not just chat. Make the model return JSON.

Problem: LLMs return text. You need structured data.

Solution: Use structured output (schema validation).

from anthropic import Anthropic
import json

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": "Extract the name and age from: John Smith is 28 years old."
    }],
    # Tell the model the format you want
    system="""You must respond with valid JSON in this format:
    {
        "name": "string",
        "age": "number"
    }
    """
)

text = response.content[0].text
data = json.loads(text)
print(data["name"])  # "John Smith"
print(data["age"])   # 28

Better approach (TypeScript/Zod style schema):

from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

# Your system prompt tells the model: "Respond with valid JSON that matches this schema"
# Then you parse and validate

Use cases:

Extracting entities from text
Categorizing user input
Generating structured reports
Creating API payloads

Week 1: Day 7 - Production Patterns

Now you have a working app. Make it production-ready.

Pattern 1: Error Handling

from anthropic import RateLimitError, APIError

try:
    response = client.messages.create(...)
except RateLimitError:
    print("Rate limited. Retry in 60 seconds")
except APIError as e:
    print(f"API error: {e.status_code}")

Pattern 2: Cost Tracking

Always log token usage:

response = client.messages.create(...)
print(f"Input tokens: {response.usage.input_tokens}")
print(f"Output tokens: {response.usage.output_tokens}")
print(f"Cost: ${(response.usage.input_tokens * 3 + response.usage.output_tokens * 15) / 1_000_000:.4f}")

Pattern 3: Caching for Cost Reduction

If you send the same context repeatedly (e.g., uploading a 100-page document), use prompt caching:

# First request (full cost)
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": "You are an expert analyst."
        },
        {
            "type": "text",
            "text": "Here is the document:\n" + huge_document,
            "cache_control": {"type": "ephemeral"}  # Cache this!
        }
    ],
    messages=[{"role": "user", "content": "Summarize this."}]
)

# Second request (90% cheaper for the cached part)
response2 = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    system=[
        {"type": "text", "text": "You are an expert analyst."},
        {
            "type": "text",
            "text": "Here is the document:\n" + huge_document,
            "cache_control": {"type": "ephemeral"}  # Reuse cache!
        }
    ],
    messages=[{"role": "user", "content": "Extract key metrics."}]
)

Pattern 4: Async for Scalability

Don’t wait for API responses in a loop:

import asyncio

async def ask_question(question):
    response = await client.messages.create(...)
    return response.content[0].text

# Ask 100 questions concurrently
results = await asyncio.gather(*[ask_question(q) for q in questions])

Going Deeper

If you want more advanced patterns:

RAG Architecture - Vector databases, chunking strategies
Agents & Frameworks - CrewAI, LangChain, autonomous agents
Prompt Engineering - Optimization techniques

Tools for your app:

Tools & Platforms Guide - Vector DBs, frameworks, monitoring
Models Guide - Which model for your use case

Next steps:

Build a small prototype (RAG chatbot on your docs)
Deploy it (Vercel, Railway, AWS Lambda)
Add monitoring (error tracking, cost analysis)
Scale it (optimize costs, add caching)