10 Must-Try AI Tools Every Developer Should Know in 2025

10 Must‑Try AI Tools Every Developer Should Know in 2025

AI has moved from novelty to necessity. The best engineers I work with treat AI like any other power tool: they standardize how they use it, automate repetitive work, and measure impact. Below are ten AI tools and patterns that have consistently improved my throughput and code quality, with concrete examples you can adapt.

1) GitHub Copilot or Codeium for in‑editor generation

Why it matters

Inline code suggestions reduce boilerplate and keep you in flow. The key is using structured prompts and accepting small, reviewable changes.

Example: Generate tests for a utility

// price.ts
export function priceWithTax(base: number, rate = 0.085) {
  if (base < 0) throw new Error('NEGATIVE');
  return Math.round((base * (1 + rate)) * 100) / 100;
}

Use your AI assistant to scaffold Jest tests, then refine:

// price.test.ts
describe('priceWithTax', () => {
  it('applies default rate', () => {
    expect(priceWithTax(100)).toBe(108.5);
  });
  it('supports custom rate', () => {
    expect(priceWithTax(100, 0.2)).toBe(120);
  });
  it('guards negatives', () => {
    expect(() => priceWithTax(-1)).toThrow('NEGATIVE');
  });
});

2) ChatGPT or Claude for architecture review and refactors

Why it matters

Long‑context models are great for reasoning about module boundaries, naming, and risks. Paste key files or link a repo, then request specific outcomes.

Example prompt

Refactor plan for `orders/` to improve testability.
- Identify seams for dependency injection.
- Suggest smaller functions with explicit inputs and outputs.
- List risks and a migration sequence with checkpoints.

3) OpenAI Assistants or AI CLI for scripted workflows

Why it matters

Wrap common AI tasks in scripts so they’re reproducible in CI and across teammates.

Example: Node script to summarize a PR

// scripts/summarize-pr.ts
import OpenAI from 'openai';
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY! });

async function summarize(title: string, diff: string) {
  const prompt = `Summarize this PR for reviewers. Title: ${title}\nDiff:\n${diff}`;
  const res = await client.chat.completions.create({ model: 'gpt-4o-mini', messages: [{ role: 'user', content: prompt }] });
  console.log(res.choices[0].message.content);
}

Run this in pre‑PR to auto‑fill description.

4) LangChain or LlamaIndex for retrieval‑augmented generation (RAG)

Why it matters

Connect internal docs, ADRs, and API contracts to your AI prompts for grounded answers.

Example: Quick RAG with TypeScript

import { ChatOpenAI } from '@langchain/openai';
import { MemoryVectorStore } from 'langchain/vectorstores/memory';
import { OpenAIEmbeddings } from '@langchain/openai';

const store = await MemoryVectorStore.fromTexts(
  ["ADR-42: Use feature flags", "Runbook: payments"],
  [{ id: 'adr-42' }, { id: 'runbook-payments' }],
  new OpenAIEmbeddings()
);
const retriever = store.asRetriever(3);
const llm = new ChatOpenAI({ modelName: 'gpt-4o-mini' });

5) Code review copilots

Why it matters

AI first‑pass reviews catch low‑hanging fruit and standardize feedback tone. Human review still owns design and risk.

Example checklist output

• Missing null checks on external inputs

• Function does two concerns, suggest extracting validator

• Add test for edge case: empty items

6) Test generators and property‑based testing helpers

Why it matters

Use AI to propose input edge cases and turn them into fast property‑based tests.

Example: fast‑check harness

import fc from 'fast-check';
import { normalizeEmail } from './normalize';

test('normalizeEmail is idempotent', () => {
  fc.assert(fc.property(fc.emailAddress(), e => normalizeEmail(normalizeEmail(e)) === normalizeEmail(e)));
});

Prompt your model: “List 10 pathological email strings we should explicitly test.”

7) AI for documentation and READMEs

Why it matters

Good docs multiply impact. Use AI to convert code comments and PR history into succinct READMEs.

Example prompt

Create a README for the `notifications` service from:
- these file headers
- this migration history
- these PR titles
Emphasize local setup, env vars, and failure modes.

8) Natural‑language query over logs and metrics

Why it matters

AI on top of observability tools helps answer “what changed?” faster during incidents.

Example queries

• “Show p95 latency and 5xx rate for checkout in the last 24h and annotate deploys.”

• “Which endpoints spiked allocations after build 212?”

9) Pairing with AI agents for migrations

Why it matters

Large‑scale codemods, deprecations, or framework upgrades benefit from scripted agents that run, test, and chunk changes.

Example plan

• Generate codemod with jscodeshift for API rename

• Run against 10% of repo

• Open draft PR with summary and risk notes

10) Prompt libraries and standards in your repo

Why it matters

Treat prompts like code: version them, review them, and share patterns.

Example: prompts/ folder

prompts/
  pr-summary.md
  design-review.md
  test-cases.md
  bug-report-triage.md

Each prompt should declare inputs, expected outputs, and guardrails.

Putting it together: a small, reproducible workflow

Here’s a practical CI step that uses an AI summary and risk assessment to help reviewers prioritize:

# .github/workflows/pr-helper.yml
name: pr-helper
on:
  pull_request:
    types: [opened, synchronize]
jobs:
  summarize:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: '20' }
      - run: npm ci
      - run: node scripts/summarize-pr.js "$PR_TITLE" "$(git diff origin/main...HEAD)"
        env:
          OPENAI_API_KEY: $ secrets.OPENAI_API_KEY 

This pattern keeps AI usage consistent and auditable.

Practical takeaways

• Standardize: Wrap AI tasks in scripts and workflows so results are repeatable.

• Ground answers: Use RAG with your docs to reduce hallucinations.

• Keep humans in the loop: AI is a first pass, not the final reviewer.

• Measure impact: Track time saved, defect rate, and lead time for changes.

• Respect data: Avoid sending secrets; sanitize diffs and logs.

Conclusion

The goal isn’t to replace engineering craft but to remove friction. Pick two or three of these tools, write small playbooks, and measure outcomes for a month. If cycle time drops and defect rates hold steady or improve, keep the practice. Over time, these habits compound into faster, clearer, more reliable engineering.