AI Fundamentals — Overview

What it is, why it matters, architecture and key concepts

✍️

Written by senior engineers. Reviewed for technical accuracy.· Updated 2025 · SynfraCore AI Fundamentals Team

Expert Content

AI & Machine Learning Engineering

Artificial Intelligence is transforming software engineering. As a developer or DevOps engineer today, you need to understand how to build with AI — calling APIs, building RAG systems, deploying models, and monitoring AI in production.

How a Large Language Model Works

The AI Stack for Engineers

Calling the Anthropic API

python

import anthropic

client = anthropic.Anthropic()  # Uses ANTHROPIC_API_KEY env var

# Simple completion
message = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain Docker in 3 sentences."}
    ]
)
print(message.content[0].text)

💡 Cost Estimation

Claude Sonnet: $3 per 1M input tokens, $15 per 1M output tokens

1,000 word essay ≈ 1,300 tokens ≈ $0.004 to generate.

For most applications, AI API costs are surprisingly low.

RAG vs Fine-Tuning — When to Use Each

	RAG	Fine-Tuning

|---|---|---|

Updates knowledge	Yes — just re-index	No — must retrain
Private data	Yes	Yes
Latency	Slightly higher (retrieval step)	Same as base model
Cost	Retrieval infra	Training compute
Best for	Current info, citations, Q&A	Style, format, specialized tasks

ℹ️ Start Simple

For 90% of use cases: Prompt Engineering first → RAG if you need private/current data → Fine-tuning only if still insufficient. Most teams over-engineer this.

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