5 AI Tools That 10x My Development Productivity

5 AI Tools That 10x My Development Productivity

AI has shifted from novelty to daily tooling. Over the last year, I standardized where and how I use AI so it’s reproducible across projects and teammates. Below are five tools and concrete workflows that consistently improve my throughput and quality. Each section includes prompts, code, or configs you can adapt immediately.

1) In‑editor coding assistants (Copilot / Codeium)

Why it matters

Inline suggestions help with boilerplate, API ergonomics, and tests. The trick is to scope prompts and accept small, reviewable diffs.

Example: Generate robust tests for a pure function

// src/lib/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;
}

Ask your assistant to scaffold edge‑case tests; then refine:

// src/lib/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('throws on negatives', () => {
    expect(() => priceWithTax(-1)).toThrow('NEGATIVE');
  });
});

Practical prompt

• “Generate Jest tests for edge cases including negatives, decimals, and custom rates. Keep tests independent and deterministic.”

2) Chat assistants for design reviews (ChatGPT / Claude)

Why it matters

Long‑context models are great for architecture discussions, naming, and risk analysis. Share the smallest set of files that captures the design seams.

Example prompt

You are a senior backend engineer. Review the following module boundaries for testability:
- routes/, handlers/, services/, repositories/
Identify seams for DI, suggest smaller functions with explicit inputs/outputs,
and outline a migration plan with checkpoints.

Output to look for

• Clear dependency graph and suggestions for inversion

• Smaller function boundaries and DTOs

• Risks and rollout plan you can paste into an RFC

3) PR summarizers and checklists in CI

Why it matters

AI can create consistent summaries and risk notes so reviewers focus on what matters.

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! });

export async function summarizePR(title: string, diff: string) {
  const prompt = `Summarize this PR for reviewers. Title: ${title}\nDiff:\n${diff}\n` +
    `Output headings: Summary, Risks, Tests to add, Manual checks.`;
  const res = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: prompt }]
  });
  return res.choices[0].message.content;
}

GitHub Action step

# .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
      - name: Summarize PR
        run: node scripts/summarize-pr.js "$PR_TITLE" "$(git diff origin/main...HEAD)"
        env:
          OPENAI_API_KEY: $ secrets.OPENAI_API_KEY 

4) Retrieval‑augmented generation (RAG) for grounded answers

Why it matters

Point your model at ADRs, runbooks, and API contracts to reduce hallucinations and speed onboarding.

Minimal TypeScript RAG snippet

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

const docs = [
  'ADR-042 Use feature flags for risky changes',
  'Runbook: payments service, env vars, alerts',
];
const store = await MemoryVectorStore.fromTexts(docs, [{ id: 'adr' }, { id: 'runbook' }], new OpenAIEmbeddings());
const retriever = store.asRetriever(3);
const llm = new ChatOpenAI({ modelName: 'gpt-4o-mini' });

Useful prompt

• “Using retrieved docs only, propose a rollback plan for the new checkout flow. Include owner, steps, and verification.”

5) Test case generators and property‑based testing helpers

Why it matters

AI can enumerate edge cases you then lock in with 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 => {
    const once = normalizeEmail(e);
    const twice = normalizeEmail(once);
    return once === twice;
  }));
});

Prompt ideas

• “List 15 pathological email inputs to test normalization and why each matters.”

• “Suggest invariants for a cart pricing function with discounts and tax.”

Putting it together: a repeatable daily workflow

1. In the editor: use Copilot/Codeium for scaffolding and tests, commit in small chunks.

1. RAG: ask design and incident questions grounded in your repo docs.

1. PRs: auto‑summarize diffs with risks and manual checklists.

1. Tests: use AI to propose edge cases, implement as property‑based tests.

1. Docs: convert PR history and code comments into READMEs for teammates.

Practical takeaways

• Standardize: wrap prompts in scripts and GitHub Actions for reproducibility.

• Grounding: connect your docs to the model to avoid speculative outputs.

• Keep humans responsible: AI is a first pass; engineering judgment ships.

• Measure: track lead time for changes, review latency, and escaped defects.

• Data hygiene: never send secrets; sanitize diffs and logs.

Conclusion

AI won’t replace engineering fundamentals. It amplifies them. Start by formalizing two or three workflows from above and measure for a month. If throughput rises and quality holds or improves, keep the practice and document it for the team. These habits compound into faster, clearer, and more reliable delivery.