How can developers use AI in their work?

Question

Accepted Answer

Developers can use AI both as a **tool to boost their own productivity** (AI coding assistants) and as a **capability to build into applications** (integrating AI features via APIs). Understanding both is increasingly valuable as AI transforms software development.

## AI as a developer productivity tool

```text
✓ AI CODING ASSISTANTS → GitHub Copilot, Cursor, etc. → code completion, generation,
  explanation → boost productivity
✓ CHAT assistants (ChatGPT, Claude) → ask coding questions, debug, explain concepts,
  generate code, learn, write tests/docs
✓ uses: write boilerplate, explain unfamiliar code, debug errors, refactor, learn new tech
⚠️ but → REVIEW AI-generated code (it can be wrong/insecure); understand what you use;
  AI is a tool, not a replacement for understanding
```

## Building AI into applications

```text
✓ Use AI APIs → integrate LLMs (OpenAI, Anthropic APIs) into apps → add AI features
  (chatbots, summarization, search, content generation, etc.) without ML expertise
✓ Use pre-built AI services → vision, speech, translation APIs (cloud AI services)
✓ RAG, embeddings, vector search → build AI features over your own data
→ developers increasingly ADD AI capabilities to applications (via APIs, not training models)
```

## Why this matters for developers

```text
→ AI is transforming HOW developers work (productivity tools) AND WHAT they build (AI features)
→ AI literacy is increasingly valuable/expected → learn to use AI tools and integrate AI
→ use AI effectively and responsibly (review output, understand limits)
```

## Why it matters

Understanding how developers can use AI is valuable, increasingly-essential knowledge because **AI is transforming both how developers work and what they build**, so AI literacy is increasingly valuable and expected.

Developers can use AI in two important ways.

As a **productivity tool** — AI coding assistants (GitHub Copilot, Cursor) for code completion and generation, and chat assistants (ChatGPT, Claude) for coding questions, debugging, explanation, and learning — AI can significantly boost productivity (writing boilerplate, explaining code, debugging, refactoring), though with the important caveat to **review AI-generated code** (which can be wrong or insecure) and treat AI as a tool, not a replacement for understanding.

As a **capability to build into applications** — using AI APIs (OpenAI, Anthropic) to integrate LLMs into apps for features like chatbots, summarization, and content generation without ML expertise, using pre-built AI services (vision, speech, translation), and using RAG, embeddings, and vector search to build AI features over your own data — developers increasingly add AI capabilities to applications (via APIs, not training models).

Understanding **why this matters** — that AI is transforming how developers work (productivity tools) and what they build (AI features), making AI literacy increasingly valuable and expected, and that developers should use AI effectively and responsibly (reviewing output, understanding limits) — clarifies AI's growing importance to developers.

As AI becomes integral to software development, understanding how to use AI tools and integrate AI capabilities is an increasingly essential developer skill.

Since AI is transforming both how developers work (productivity tools) and what they build (AI features integrated via APIs), and since AI literacy (using AI tools effectively and responsibly, integrating AI capabilities) is increasingly valuable and expected, understanding how developers can use AI is valuable, increasingly-essential knowledge — important for the dual role of AI in development (boosting productivity and enabling AI features), increasingly expected developer literacy, and practical knowledge for using AI effectively and responsibly as it transforms software development.