Best AI tools for software engineers

Software engineering is already a stack of context switching: tickets, code review, tests, docs, incident notes, and the occasional “why does this only fail in CI?” mystery.

AI tools won’t replace engineering judgment. The useful ones do something simpler: they get the busywork out of your way.

• Faster first drafts of boilerplate
• Quicker understanding of unfamiliar modules
• Better checklists for review and testing
• Less time staring at a blank doc page

Treat AI like a junior teammate: helpful, fast, occasionally wrong. You get the most value when you use it for small, reviewable chunks.

At a glance

• Best for: IDE pair-programming, navigating unfamiliar code, drafting tests/docs, code review checklists
• Great first stack: one in-IDE assistant (Copilot/JetBrains AI/Cursor) + one general assistant (ChatGPT/Claude)
• Use AI for: drafts, explanations, and checklists
• Hard guardrails: don’t accept code you don’t understand; don’t paste secrets; keep diffs small; run tests

What to aim AI at

High-leverage use cases

• autocomplete for repetitive patterns (API clients, glue code, test scaffolding)
• “explain this file/function” when you’re new to a module
• generating test matrices and edge-case lists
• drafting PR descriptions, release notes, and runbooks

Risky use cases

• large refactors generated in one shot
• security-sensitive code written without review
• code you can’t explain or test

Tool picks (with rationale)

1) GitHub Copilot: IDE pair-programmer

Strong at autocomplete and quick inline suggestions in VS Code, JetBrains, and other editors.

Why this pick: maximum speed with minimal context switching.

Best for: tests, boilerplate, small helper functions.

2) JetBrains AI Assistant: IDE-native assistance

If your team lives in IntelliJ/PyCharm/etc., staying inside the IDE reduces friction.

Why this pick: it’s easier to ask questions with the code context right there.

3) Cursor: repo-aware chat and multi-file edits

Useful when you need guided changes across multiple files.

Why this pick: speeds up “make this change in these places,” but only if you keep the diff reviewable.

4) Codeium: autocomplete + chat (cross-editor)

A practical alternative for day-to-day help.

Why this pick: broad editor support and quick suggestions.

5) Sourcegraph Cody: codebase-aware search + chat

Repo-scale context matters in monorepos or multi-service systems.

Why this pick: helps answer “where is this implemented?” without manual hunting.

6) ChatGPT: general reasoning and drafting

Excellent for code-adjacent work: docs, RFCs, release notes, and translating patterns between languages.

7) Claude: long-context analysis and writing

Strong for digesting long specs, logs, and larger snippets to produce plans and clear writing.

8) Snyk Code (and similar): security scanning

Security tools catch common issues earlier in PRs and CI.

Why this pick: pushes risk discovery left, when fixes are cheapest.

Step-by-step workflow (ship faster without lowering quality)

Step 1: Ask for a plan before code

Prompt:

“Propose a minimal plan for implementing X. List files likely touched, risks, and tests to add.”

Pick the approach yourself.

Step 2: Generate small chunks

Rule of thumb: if the change is too big to review comfortably, it’s too big to ask AI to generate.

Step 3: Verify like you normally would (maybe more)

• read the diff
• run tests
• add/adjust tests for edge cases
• confirm error paths, logging, and metrics

Step 4: Use AI to improve communication

Ask for:

• PR descriptions (context, approach, testing, rollout)
• changelog entries
• incident debrief templates

This is where the model is often the most dependable.

Step 5: Keep security and privacy boring and strict

• don’t paste secrets or customer data
• prefer enterprise-approved tooling for proprietary code
• run scanning in CI

Concrete examples

Example: prompts that reduce review risk

• “List edge cases and negative paths for this function.”
• “What could go wrong in production? Focus on concurrency, retries, idempotency.”
• “Suggest tests for the behavior change described in this PR.”

Example: shrinking a risky change

Instead of “refactor the whole module,” ask:

• “extract this function”
• “add tests around current behavior”
• “make one behavior change”

Small diffs are safer diffs.

Mistakes to avoid

• Accepting code you don’t understand. Fast isn’t helpful if it adds hidden risk.
• Letting AI rewrite large surfaces. Keep diffs small and stage refactors.
• Sharing secrets or proprietary data in unapproved tools. Treat prompts like external communication unless you have approved controls.
• Skipping security checks. AI output can include insecure patterns.

FAQ

Will these tools make me slower because I have to review more?

Sometimes, especially at the start. Use AI for small, testable chunks so review stays comfortable.

Is AI autocomplete safe for production code?

It can be, if you treat suggestions like junior-dev output: helpful, fast, and occasionally wrong. Keep tests, linting, and code review.

Can I use AI with proprietary code?

That depends on your company policies and the tool’s enterprise controls. If you aren’t sure, assume “no” and check.

What’s the simplest setup that works?

Pick one in-IDE assistant (Copilot or JetBrains AI) and one general assistant (ChatGPT or Claude). Add repo-scale search only if you regularly struggle to find things.

Try these walkthroughs

• Code review
• Test generation

Closing thought

AI tools help most when they behave like focused teammates: quick first drafts, fast explanations, and gentle nudges toward better tests and clearer docs. If a tool makes you accept code you don’t understand, it’s not helping.