LEVEL 1

Using AI

Master the fundamentals that 90% of developers get wrong

Originally inspired by Zach Wilson (@eczachly)'s insights on AI Engineering levels

Weeks

80%

Cost Savings

$143K

Starting Salary

What You'll Master

🎯

Prompt Engineering

Zero-shot, few-shot, and chain-of-thought techniques. Learn Tree of Thoughts (ToT) and Graph of Thoughts (GoT) for complex reasoning.

🔌

API Integration

Master OpenAI, Anthropic, Cohere, and Hugging Face APIs. Handle rate limiting, retries, and error handling like a pro.

💰

Cost Optimization

Reduce API costs by 80% with caching, batching, and model selection strategies. Learn when to use GPT-4 vs GPT-4-mini.

🎛️

Parameter Tuning

Master temperature, top-p, and token management. Learn optimal settings for different use cases.

📊

Evaluation Metrics

Implement BLEU, ROUGE, and perplexity scores. Build A/B testing frameworks for prompt optimization.

🚀

Production Apps

Build real applications: chatbots, content generators, and document summarizers that actually work.

Real Code You'll Write

Production-Ready API Integration

import openai
from typing import List, Dict
import asyncio

class ProductionAIClient:
    def __init__(self, api_key: str):
        self.client = openai.OpenAI(api_key=api_key)
        self.cache = {}

    async def complete(self, prompt: str, **kwargs) -> str:
        # Smart caching to reduce costs
        cache_key = f"{prompt}_{kwargs}"
        if cache_key in self.cache:
            return self.cache[cache_key]

        response = await self.client.chat.completions.create(
            model=kwargs.get('model', 'gpt-4o-mini'),
            messages=[{"role": "user", "content": prompt}],
            temperature=kwargs.get('temperature', 0.7),
            max_tokens=kwargs.get('max_tokens', 500)
        )

        result = response.choices[0].message.content
        self.cache[cache_key] = result
        return result

# Save 80% on API costs with smart model selection
def select_model_by_task(task_complexity: str) -> str:
    model_map = {
        "simple": "gpt-3.5-turbo",     # $0.002/1K tokens
        "moderate": "gpt-4o-mini",      # $0.015/1K tokens
        "complex": "gpt-4o",            # $0.03/1K tokens
        "specialized": "o1-preview"     # Advanced reasoning
    }
    return model_map.get(task_complexity, "gpt-4o-mini")

Projects You'll Build

💬

Week 1-2: Smart Chatbot

Build a context-aware chatbot that handles 1000+ concurrent users with sub-second response times.

📝

Week 3-4: Content Generator

Create a system that generates SEO-optimized content, saving 70% of writing time like Grupo Nutresa.

📄

Week 5-6: Document Summarizer

Process 100-page documents in seconds, maintaining key information with 95% accuracy.

🔍

Week 7-8: AI Search Engine

Combine multiple APIs to build a semantic search engine that understands context and intent.