How to Actually Use LLMs for Coding in 2025

Most Developers Are Still Underusing LLMs — Here is How to Fix That

Despite the explosion of AI coding tools in 2025, a surprising number of developers are still stuck in what some call 'old-school prompting' — copying code into ChatGPT's web interface and pasting responses back into their editor. The gap between how most people use LLMs and how power users leverage them has never been wider, and closing that gap can dramatically boost productivity.

A recent discussion in developer communities highlights a common pattern: programmers know powerful AI tools exist but feel overwhelmed by the options, unsure which setup delivers the best results, and confused about concepts like API proxies and agentic coding assistants. This guide breaks down the modern LLM coding toolkit and explains exactly how each piece fits together.

Key Takeaways

• VS Code's built-in AI features (Copilot, Copilot Chat, and Copilot Agent Mode) offer the lowest friction entry point for most developers
• CLI-based agents like Claude Code and OpenAI Codex represent the next level of AI-assisted development
• API proxy services unlock access to multiple models through a single endpoint, powering dozens of third-party tools
• The best workflow in 2025 combines at least 2-3 tools for different tasks
• Most AI coding tools now accept custom API endpoints, making proxy services more useful than ever
• Switching from chat-based prompting to agentic workflows can cut development time by 30-50% on routine tasks

VS Code Remains the Best Starting Point for AI-Assisted Coding

Visual Studio Code continues to dominate as the most popular code editor, and its AI integration has matured significantly. For developers who already live in VS Code, the path of least resistance is leveraging the AI features built right into the editor.

GitHub Copilot remains the gold standard for inline code completion. At $10/month for individuals (or free for verified students and open-source maintainers), it provides real-time suggestions as you type. But inline completion is just the beginning.

Copilot Chat lets you have conversational interactions with an LLM directly in VS Code's sidebar. You can ask it to explain code, debug errors, generate tests, or refactor functions — all without leaving your editor. This alone replaces the need to copy-paste code into ChatGPT's web interface.

The real game-changer is Copilot Agent Mode (previously called Copilot Edits), which launched broadly in early 2025. Agent Mode can:

• Read and modify multiple files simultaneously
• Run terminal commands and interpret their output
• Iterate on errors automatically by reading compiler or test output
• Create entire features across your codebase with a single prompt
• Install dependencies and configure build tools

Agent Mode transforms VS Code from a 'smart autocomplete' tool into a semi-autonomous coding partner. Unlike the older chat-based workflow, Agent Mode takes actions — it edits files, runs your code, checks results, and adjusts its approach.

CLI Agents: Claude Code and OpenAI Codex Push the Boundaries

Beyond VS Code, a new category of command-line AI agents has emerged as the power user's tool of choice. The 2 most prominent are Claude Code from Anthropic and OpenAI Codex CLI.

Claude Code runs directly in your terminal and operates as an agentic coding assistant. It can navigate your project, read files, write code, run tests, and even make git commits. Unlike a chat interface, Claude Code understands your entire project context and works within it. The tool requires an Anthropic API key (or a Claude Pro/Max subscription at $20-$200/month), and it excels at complex, multi-file tasks.

Setting up Claude Code is straightforward:

• Install via npm install -g @anthropic-ai/claude-code
• Navigate to your project directory
• Run claude and start describing what you want to build
• The agent reads your codebase, proposes changes, and executes them with your approval

OpenAI Codex CLI takes a similar approach but is powered by OpenAI's models. It offers a lightweight terminal experience and integrates well with existing OpenAI API keys. Codex focuses on sandboxed execution, meaning it runs code in isolated environments for safety.

The key insight is that these CLI tools are not replacements for VS Code — they are complementary. Many developers use VS Code for day-to-day editing with Copilot, then switch to Claude Code or Codex for larger refactoring tasks, new feature development, or complex debugging sessions.

Understanding API Proxies: The Hidden Infrastructure Layer

One of the most confusing concepts for newcomers is the API proxy (sometimes called an 'API relay' or 'API gateway'). These services act as intermediaries between your tools and the actual LLM providers like OpenAI, Anthropic, or Google.

Here is how API proxies work in practice:

• You sign up for a proxy service (examples include OpenRouter, LiteLLM, or various community-run relays)
• The proxy gives you a single API key and a unified endpoint
• You configure your tools to point to the proxy's URL instead of the official API endpoint
• The proxy routes your requests to the appropriate model provider

Why would you want this? Several compelling reasons:

• Access to multiple models through one account (GPT-4o, Claude 3.5 Sonnet, Gemini 2.5 Pro, Llama 4, and more)
• Cost optimization — some proxies offer better rates or allow you to set spending limits
• Fallback routing — if one provider is down, the proxy can automatically switch to another
• Regional access — developers in regions where certain providers are unavailable can use proxies to access those models
• Consolidated billing — one invoice instead of managing accounts with 5 different providers

Contrary to what some developers assume, most modern AI coding tools absolutely do accept custom API endpoints. Tools like Continue.dev, Cline, Aider, Open WebUI, and even some configurations of Copilot allow you to specify a custom base URL for API calls. This is exactly where proxy services become valuable.

The Modern Developer's AI Toolkit: A Practical Setup

So what does an optimized 2025 AI coding workflow actually look like? Based on patterns emerging from thousands of developers, here is a practical setup that balances power, cost, and convenience.

Tier 1 — Always On (Low Friction)
VS Code with GitHub Copilot handles your minute-to-minute coding. Inline completions fire automatically, and Copilot Chat is always a keyboard shortcut away for quick questions. Cost: $10/month.

Tier 2 — Medium Tasks (Moderate Effort)
For tasks that span multiple files or require deeper reasoning, switch to Copilot Agent Mode or install Cline / Continue.dev as VS Code extensions. These extensions let you use any model via custom API endpoints, including proxy services. This is where API proxies shine — you can point Cline at OpenRouter and switch between Claude 3.5 Sonnet for complex logic and GPT-4o-mini for simple tasks, optimizing cost.

Tier 3 — Heavy Lifting (Maximum Power)
For major features, large refactors, or greenfield projects, fire up Claude Code or Codex in the terminal. Let the agent work through the problem autonomously while you review its proposed changes. This tier handles tasks that would take hours manually but can be completed in minutes with an agentic approach.

Beyond Chat: Tools That Consume LLM APIs

The ecosystem of tools that leverage LLM APIs extends far beyond simple chat interfaces. Understanding this landscape helps you get more value from API keys and proxy subscriptions.

Code-focused tools:
- Aider — a terminal-based pair programming tool that works with git, supporting Claude, GPT, and local models
- Cursor — a fork of VS Code with deeply integrated AI features, starting at $20/month
- Windsurf — another AI-native editor (formerly Codeium), competitive with Cursor at similar price points
- Cline — a VS Code extension that turns any model into an autonomous coding agent

General productivity tools:
- Open WebUI — a self-hosted chat interface that connects to any OpenAI-compatible API
- TypingMind — a polished chat client that supports custom API endpoints and multiple model providers
- Jan — an open-source desktop app for running and chatting with LLMs locally or via API

All of these tools accept custom API endpoints, which means a single proxy service subscription can power your entire toolkit. The days of being locked into one provider's ecosystem are over.

Common Mistakes Developers Make With LLM Tools

After observing how thousands of developers adopt AI coding tools, several patterns of suboptimal usage emerge consistently.

Mistake 1: Using chat interfaces for everything. The ChatGPT web interface is great for learning and exploration, but it is the least efficient way to write production code. Context switching between browser and editor kills flow state.

Mistake 2: Not providing enough context. LLMs produce dramatically better code when they can see your existing codebase, your test files, your configuration, and your coding style. Tools like Claude Code and Copilot Agent Mode provide this context automatically — manual copy-paste never can.

Mistake 3: Ignoring model selection. Different models excel at different tasks. Claude 3.5 Sonnet is widely regarded as superior for complex coding tasks compared to GPT-4o, while GPT-4o-mini offers excellent performance for simple tasks at a fraction of the cost. Using a proxy service makes switching between models trivial.

Mistake 4: Not reviewing AI-generated code. Blindly accepting AI suggestions leads to subtle bugs, security vulnerabilities, and technical debt. Always review, test, and understand the code your AI tools produce.

Looking Ahead: Where AI Coding Tools Are Heading

The trajectory of AI coding tools points toward increasingly autonomous agents that require less manual intervention. OpenAI's recent launch of Codex as a cloud-based agent that runs in the background and delivers completed pull requests hints at a future where developers spend more time reviewing and directing AI work than writing code themselves.

Several trends are worth watching:

• Model costs continue to drop — GPT-4o-level performance that cost $30 per million tokens in 2023 now costs under $3, making heavy API usage increasingly affordable
• Agent frameworks are maturing — tools like Claude Code already handle multi-step tasks that would have been impossible 12 months ago
• Local models are catching up — Llama 4 and Qwen 3 models running on consumer hardware can handle many coding tasks that previously required cloud APIs
• IDE integration is deepening — the line between 'editor' and 'AI agent' is blurring rapidly

The bottom line? If you are still manually copying code into a chat window in 2025, you are leaving enormous productivity gains on the table. The tools exist, most are affordable or free, and the learning curve is measured in hours, not weeks. Start with VS Code's built-in AI features, experiment with one CLI agent, and explore the API proxy ecosystem when you are ready to level up.