AI for Developers: Essential Tools and Resources

AI Tools Every Developer Should Know

AI is transforming software development. From intelligent code completion to automated testing to building AI-powered features, here's your comprehensive guide to AI tools for developers.

Coding Assistants

Comparison

What They Do

APIs and Models

LLM APIs

Quick Start: OpenAI

from openai import OpenAI

client = OpenAI(api_key="...")

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

Quick Start: Anthropic

import anthropic

client = anthropic.Anthropic(api_key="...")

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.content[0].text)

Frameworks

LLM Orchestration

LangChain Example

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{input}")
])
chain = prompt | llm

response = chain.invoke({"input": "Hello!"})
print(response.content)

AI Web Development

Vector Databases

Quick Comparison

Basic RAG Pattern

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

# Create embeddings and store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)

# Query
results = vectorstore.similarity_search("my question", k=5)

Infrastructure

Deployment Options

GPU Compute

Observability

Debugging and Monitoring

Open Source Models

Running Locally

Best Open Models

Testing and Evaluation

Tools

Basic Evaluation Pattern

from promptfoo import evaluate

results = evaluate(
    prompts=["Translate: {text}"],
    providers=["openai:gpt-4o", "anthropic:claude-3"],
    tests=[
        {"vars": {"text": "Hello"}, "assert": {"type": "contains", "value": "Bonjour"}}
    ]
)

Best Practices

API Usage

Security

Performance

Building AI Features

Checklist

• Define the problem clearly
• Choose the right model/API
• Handle errors gracefully
• Implement streaming
• Monitor usage and costs
• Test edge cases
• Get user feedback
• Iterate and improve

"The best AI features solve real problems, handle failure gracefully, and improve over time. Start simple, ship fast, and iterate based on real usage."