LLM Best Practices: How to use AI in software development without losing quality or control
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From one dev to another: we’ve all dreamed of asking the AI, “Build me the microservice,” only to get an 800-line diff nobody wants to review. The truth? LLMs for coding work best when they think small, with precise context, clear expectations, and a safety net made of tests and code reviews.
Today we’re skipping the hype. You’ll see patterns, anti-patterns, and prompts you can copy and paste. If you’ve ever wondered how to use LLMs in a real sprint (not a perfect demo), the answer is in process: define scope, give signals (architecture, standards, invariants), demand minimal diffs, and measure impact in PRs.
Goal: adopt LLM best practices that accelerate development without introducing bugs, rollbacks, or phantom dependencies. If by the end your team can ask the AI for a focused change with tests and ship it through CI/CD smoothly, we win.
LLM Best Practices for Developers
Prompt Engineering (Not Poetry)
Principle: context > creativity. The better the context, the better the code.
- Define a role (“senior developer in {stack} focused on security/performance”).
- Summarize context (module, version, lint/test standards).
- Limit the task to one responsibility and <50 lines.
- Request output as unified diff + tests.
- Add acceptance criteria (no new dependencies, acceptable complexity, SLA).
Quick Template (Copy/Paste):
Role: Senior developer in {stack}, focused on {security/performance}.
Context: {module, framework, version}, standards {lint, tests, CI}, constraints {latency, memory}.
Task: {specific, small}
Input: {relevant file/snippet}
Output: unified diff + {n} tests
Criteria: no new deps, complexity < N, min. coverage
Do not: {out of scope}
This structure forces the model to produce reviewable LLM code, no walls of text, just minimal patches, short explanations, and a focus on contracts.
Divide and Conquer (Small Tasks Only)
Ask for single functions, not full services. Keep one responsibility per prompt. If the solution grows complex, stop and ask for the reasoning before generating more code.
Smaller scope = smaller diffs, fewer hallucinations, faster reviews.
That’s the essence of LLM best practices for coding: incremental changes, continuous verification, and clear intent , as Phillip Carter puts it, “ask for small code changes, not big ones.”
Useful Context Without Blowing the Window
- Include public signatures, DTOs, feature flags, and failing tests.
- Avoid pasting the entire repo, use only what’s needed.
The model doesn’t need your monolith; just enough to “compile in its head.” Less noise = better answers.
RAG: Retrieval Before Generation
Fetch internal docs first (snippets, ADRs, style guides) and instruct the model to respond only using that evidence.
If info is missing, it should state that clearly and ask for the minimal context it needs.
This retrieval-augmented generation approach grounds LLM use in real enterprise workflows,reducing hallucinations, aligning code with your standards, and improving traceability and review.
In short: retrieve, then generate.
Safety Net: Tests and Verification
- Unit and integration tests are mandatory.
- Static analysis: linters, type checks, SAST.
- Run locally before PR.
Prompt Snippet (Tests First):
Write 5 test cases that break my implementation.
Cover nulls, timeouts, limits, and provider errors.
Return only the test file.
If you can’t test it, you can’t maintain it. That’s the practical way to use LLMs without fear
Security and Privacy (Minimum Policy)
Never paste secrets or PII into a prompt. Use vaults or redaction instead. Before integrating any provider, verify that training is disabled (training=off).
All generated code must pass SAST/DAST in CI, and you can add a prompt audit diff to detect vulnerabilities like SQLi, XSS, SSRF, hardcoded secrets, or path traversal. Security shift-left applies to LLMs too: skip it early, and you’ll pay later with incidents, tech debt, and compliance gaps.
Intentional Design (Architecture Over Guesswork)
Request explicit patterns: hexagonal, circuit breaker, idempotency. Define no-gos: reflection, global singletons, “magic” dependencies.
The AI should play on your field, not invent its own. That’s one of the most ignored LLM best practices.
Metrics That Actually Matter
Track the real impact of AI through:
- PR lead time (with vs. without LLMs)
- First-try approval rate
- Post-merge defects or rollbacks
- Test coverage and mutation score
These show tangible value, not just code volume. Forget “lines generated.” What matters are outcomes, not outputs. That’s how to justify LLM usage responsibly to engineering and product teams.
Anti-Patterns (Avoid These)
- Build the entire microservice.”
- Pasting huge stack traces + half the repo.
- Three unrelated tasks in one prompt.
- Blind trust without tests or CI.
If you see yourself here, pause, cut the scope, and go back to minimal diffs.
Integrate into CI/CD (Real Workflow)
- Pre-commit: auto-format, lint, and run a secrets scan.
- CI: run tests, SAST, and dependency license checks.
- PR template: add checkboxes for “AI-generated,” “has tests,” “risks,” and “rollout plan.”
- Deployment: control exposure with feature flags for safe, gradual rollouts.
This pipeline turns demos into real deliverables. From dev to dev, this is where the value lives.
Example (Node + Jest)
Prompt:
Refactor retryFetch(url, opts) to add exponential backoff (max 5 attempts), cancel in 1.5s, and log only warnings on retries. Output unified diff + 4 Jest tests. No new dependencies.
Review Checklist:
- Handles timeouts and retry caps?
- No new dependencies?
- Tests cover edge cases (timeout on attempt 1 and 5)?
- Logs follow team standards?
Internal Policy (8 Lines, Paste-Ready)
- AI is an assistant, not a source of truth.
- Never paste secrets/PII.
- All generated code requires tests and review.
- No new dependencies without RFC/issue.
- Request small patches (unified diff).
- Use RAG when internal docs exist.
- Always run linters, SAST, and tests in CI.
- We measure quality and flow, not lines.
What Really Matters When Using AI in Your Workflow
If your team adopts LLMs with structured prompts, small diffs, RAG, security practices, and clear metrics, AI becomes a true multiplier of productivity and quality. It’s not about spectacle, it’s about process.
Used wisely, AI is like pair programming on turbo, not magic.
Control context, scope, verification, and security, and you’ll gain speed without paying for it later in bugs or chaos.
Ready to apply these best practices in your daily dev work? From one developer to another: start small, measure, adjust, everything else is noise.
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