5 AI-Assisted Coding Techniques Guaranteed to Save You Time

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# Introduction

 
Most developers don’t need help typing faster. What slows projects down are the endless loops of setup, review, and rework. That’s where AI is starting to make a real difference.

Over the past year, tools like GitHub Copilot, Claude, and Google’s Jules have evolved from autocomplete assistants into coding agents that can plan, build, test, and even review code asynchronously. Instead of waiting for you to drive every step, they can now act on instructions, explain their reasoning, and push working code back to your repo.

The shift is subtle but important: AI is no longer just helping you write code; it’s learning how to work alongside you. With the right approach, these systems can save hours in your day by handling the repetitive, mechanical aspects of development, allowing you to focus on architecture, logic, and decisions that truly require human judgment.

In this article, we’ll examine five AI-assisted coding techniques that save significant time without compromising quality, ranging from feeding design documents directly into models to pairing two AIs as coder and reviewer. Each one is simple enough to adopt today, and together they form a smarter, faster development workflow.

 

# Technique 1: Letting AI Read Your Design Docs Before You Code

 
One of the easiest ways to get better results from coding models is to stop giving them isolated prompts and start giving them context. When you share your design document, architecture overview, or feature specification before asking for code, you give the model a complete picture of what you’re trying to build.

For example, instead of this:

# weak prompt
"Write a FastAPI endpoint for creating new users."

 

try something like this:

# context-rich prompt
"""
You're helping implement the 'User Management' module described below.
The system uses JWT for auth, and a PostgreSQL database via SQLAlchemy.
Create a FastAPI endpoint for creating new users, validating input, and returning a token.
"""

 

When a model “reads” design context first, its responses become more aligned with your architecture, naming conventions, and data flow.

You spend less time rewriting or debugging mismatched code and more time integrating.
Tools like Google Jules and Anthropic Claude handle this naturally; they can ingest Markdown, system docs, or AGENTS.md files and use that knowledge across tasks.

 

# Technique 2: Using One to Code, One to Review

 
Every experienced team has two core roles: the builder and the reviewer. You can now reproduce that pattern with two cooperating AI models.

One model (for example, Claude 3.5 Sonnet) can act as the code generator, producing the initial implementation based on your spec. A second model (say, Gemini 2.5 Pro or GPT-4o) then reviews the diff, adds inline comments, and suggests corrections or tests.

Example workflow in Python pseudocode:

code = coder_model.generate("Implement a caching layer with Redis.")
review = reviewer_model.generate(
  	 f"Review the following code for performance, clarity, and edge cases:\n{code}"
)
print(review)

 

This pattern has become common in multi-agent frameworks such as AutoGen or CrewAI, and it’s built directly into Jules, which allows an agent to write code and another to verify it before creating a pull request.

Why does it save time?

  • The model finds its own logical errors
  • Review feedback comes instantly, so you merge with higher confidence
  • It reduces human review overhead, especially for routine or boilerplate updates

 

# Technique 3: Automating Tests and Validation with AI Agents

 
Writing tests isn’t hard; it’s just tedious. That’s why it’s one of the best areas to delegate to AI. Modern coding agents can now read your existing test suite, infer missing coverage, and generate new tests automatically.

In Google Jules, for example, once it finishes implementing a feature, it runs your setup script inside a secure cloud VM, detects test frameworks like pytest or Jest, and then adds or repairs failing tests before creating a pull request.
Here’s what that workflow might look like conceptually:

# Step 1: Run tests in Jules or your local AI agent
jules run "Add tests for parseQueryString in utils.js"

# Step 2: Review the plan
# Jules will show the files to be updated, the test structure, and reasoning

# Step 3: Approve and wait for test validation
# The agent runs pytest, validates changes, and commits working code

 

Other tools can also analyze your repository structure, identify edge cases, and generate high-quality unit or integration tests in one pass.

The biggest time savings come not from writing brand-new tests, but from letting the model fix failing ones during version bumps or refactors. It’s the kind of slow, repetitive debugging task that AI agents handle consistently well.

In practice:

  • Your CI pipeline stays green with minimal human attention
  • Tests stay up to date as your code evolves
  • You catch regressions early, without needing to manually rewrite tests

 

# Technique 4: Using AI to Refactor and Modernize Legacy Code

 
Old codebases slow everyone down, not because they’re bad, but because no one remembers why things were written that way. AI-assisted refactoring can bridge that gap by reading, understanding, and modernizing code safely and incrementally.

Tools like Google Jules and GitHub Copilot really excel here. You can ask them to upgrade dependencies, rewrite modules in a newer framework, or convert classes to functions without breaking the original logic.

For example, Jules can take a request like this:

"Upgrade this project from React 17 to React 19, adopt the new app directory structure, and ensure tests still pass."

 

Behind the scenes, here is what it does:

  • Clones your repo into a secure cloud VM
  • Runs your setup script (to install dependencies)
  • Generates a plan and diff showing all changes
  • Runs your test suite to confirm the upgrade worked
  • Pushes a pull request with verified changes

 

# Technique 5: Generating and Explaining Code in Parallel (Async Workflows)

 
When you’re deep in a coding sprint, waiting for model replies can break your flow. Modern agentic tools now support asynchronous workflows, letting you offload multiple coding or documentation tasks at once while staying focused on your main work.

Imagine this using Google Jules:

# Create multiple AI coding sessions in parallel
jules remote new --repo . --session "Write TypeScript types for API responses"
jules remote new --repo . --session "Add input validation to /signup route"
jules remote new --repo . --session "Document auth middleware with docstrings"

 

You can then keep working locally while Jules runs these tasks on secure cloud VMs, reviews results, and reports back when done. Each job gets its own branch and plan for you to approve, meaning you can manage your “AI teammates” like real collaborators.

This asynchronous, multi-session approach saves enormous time in distributed teams:

  • You can queue up 3–15 tasks (depending on your Jules plan)
  • Results arrive incrementally, so nothing blocks your workflow
  • You can review diffs, accept PRs, or rerun failed tasks independently

Gemini 2.5 Pro, the model powering Jules, is optimized for long-context, multi-step reasoning, so it doesn’t just generate code; it keeps track of prior steps, understands dependencies, and syncs progress between tasks.

 

# Putting It All Together

 
Each of these five techniques works well on its own, but the real advantage comes from chaining them into a continuous, feedback-driven workflow. Here’s what that could look like in practice:

  1. Design-driven prompting: Start with a well-structured spec or design doc. Feed it to your coding agent as context so it knows your architecture, patterns, and constraints.
  2. Dual-agent coding loop: Run two models in tandem, one acts as the coder, the other as the reviewer. The coder generates diffs or pull requests, while the reviewer runs validation, suggests improvements, or flags inconsistencies.
  3. Automated test and validation: Let your AI agent create or repair tests as soon as new code lands. This ensures every change remains verifiable and ready for CI/CD integration.
  4. AI-driven refactoring and maintenance: Use asynchronous agents like Jules to handle repetitive upgrades (dependency bumps, config migrations, deprecated API rewrites) in the background.
  5. Prompt evolution: Feed back results from previous tasks — successes and mistakes alike — to refine your prompts over time. This is how AI workflows mature into semi-autonomous systems.

Here’s a simple high-level flow:

 

Putting-the-Techniques-TogetherImage by Author

 

Each agent (or model) handles a layer of abstraction, keeping your human attention on why the code matters

 

# Wrapping Up

 
AI-assisted development isn’t about writing code for you. It’s about freeing you to focus on architecture, creativity, and problem framing, the parts no AI or machine can replace.

If you use these tools thoughtfully, these tools turn hours of boilerplate and refactoring into solid codebases, while giving you space to think deeply and build intentionally. Whether it’s Jules handling your GitHub PRs, Copilot suggesting context-aware functions, or a custom Gemini agent reviewing code, the pattern is the same.
 
 

Shittu Olumide is a software engineer and technical writer passionate about leveraging cutting-edge technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. You can also find Shittu on Twitter.