5 AI-supported coding techniques guaranteed to save you time

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# Introduction

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

Over the past year, tools like GitHub Copilot, Cloud, and Google’s Jules have evolved from automated assistants to coding agents that can join coding agents, even look at code in a non-obtrusive way. Instead of waiting for you to run each step, they can now follow the instructions, explain their reasoning, and get the working code back to their repo.

The shift is subtle but significant: AI no longer helps you write code. It’s learning how to work with you. With the right approach, these systems can save hours in your day by dealing with the repetitive, mechanical aspects of development, allowing you to focus on the architecture, logic, and decisions that actually require human judgment.

In this article, we will examine Five AI-Assisted Coding Techniques This saves significant time without compromising quality, from feeding design documents directly into the model to adding two AIs paired as coders and reviewers. Each one is so easy to adopt today, and together they create a faster, faster development workflow.

# Technique 1: Let the AI ​​read your design documents before your code

One of the easiest ways to get better results from coding models is to stop giving them isolated cues 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:

# 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 modelreads” Before design context, its responses become more closely related to your architecture, naming conventions, and data flow.

You spend less time rewriting or debugging matching code and more time integrating.
Love the tools Google Jules And The anthropic cloud Take it naturally. They can eat Markdownfor , for , for , . System documentationor Agents. M.D files and apply this knowledge to tasks.

# Technique 2: Using a code, to perform a review

Every experience team has two primary roles: the The builder And Reviewer. Now you can reproduce this style with two collaborating AI models.

A model (for example, Claude 3.5 sonnet) can act as Code generatorcreating an initial implementation based on your hypothesis. Another model (say, Gemini 2.5 Pro or GPT-4O) then review the differences, add inline comments, and suggest fixes 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 Multi-Agent Framework Like Autogen or Curioiand it’s built directly into Jules, which allows one agent to write code and another can verify it before making a pull request.

Why does it save time?

• The model gets its logical fallacies
• Feedback comes instantly, so you integrate with high confidence
• This reduces the overhead of human review, especially for routine or boilerplate updates.

# Technique 3: Automated Test and Validation with AI Agents

Writing tests is not difficult. It’s just painful. This is why it is an excellent field to hand over to AI. Modern coding agents can now read your existing test suite, assess missing coverage, and automatically generate new tests.

In Google Jules, for example, once it finishes implementing a feature, it runs your setup script inside a secure cloud VM, such as the Test Framework detection. pytest or JESTand then adds or repairs the failed tests before building the pull request.
Here’s what this workflow looks 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 allowing the model to fail during version bumps or refactors. This is the kind of slow, repetitive debugging task that AI agents routinely handle.

In practice:

• Your CI pipeline stays green with minimal human attention
• Tests stay fresh as your code evolves
• You catch up quickly, without the need to manually rewrite the 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 this gap by reading, understanding and modernizing code safely and incrementally.

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

For example, Jules might make 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’s what it does:

• Clone your repo into one Secure cloud vm
• Runs your setup script (to install dependencies)
• Produces a Plan and difference Showing all changes
• Runs your test suite to verify the upgrade works
• Push a Application request With confirmed changes

# Technique 5: Developing and specifying code in parallel (ASYNC workflows).

When you’re deep into a coding sprint, waiting for model responses can break your flow. Modern agent tools now let you offload multiple coding or documentation tasks at once while focusing on your core 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 continue to work locally while Jules performs these tasks on secure cloud VMs, reviews the results of the review, and reports back when the work is done. Each task gets its own branch and plans for you to approve, meaning you can manage your “AI teammates“Like real friends.

This heterogeneous, multi-session approach saves a lot of time in distributed teams:

• You can queue 3-15 tasks (depending on your Jules plan)
• Results arrive slowly, so nothing interrupts your workflow
• You can review doubles, accept PRS, or recover failed tasks independently

Gemini 2.5 ProJoules powering the model, is optimized for long-context, multi-level reasoning, so it doesn’t just generate code. It keeps track of previous actions, understands dependencies, and synchronizes progress between tasks.

# Putting it all together

Each of these five techniques works well on its own, but the real benefit comes from tying them into a consistent, feedback-driven workflow. In practice it might look like this:

1. Design-based tips: Start with a well-structured spec or design doc. Feed this 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 acting as coder, the other as evaluator. The coder develops defense or pull requests, while the reviewer validates, suggests improvements, or flags inconsistencies.
3. Automated Tests and Validations: Let your AI agent build tests or repair new code as it lands. This ensures that every change is verifiable and ready for CI/CD integration.
4. AI-Drive Refactoring and Maintenance: Use conflicting agents like Julius to handle frequent upgrades (dependency bumps, config migrations, deprecated API scripts) in the background.
5. Quick evolution: Replay similar results from previous tasks – successes and mistakes – to improve your indicators over time. Similarly, AI flows mature into semi-autonomous systems.

Here is a simple high-level flow:

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Each agent (or model) handles a layer of abstraction, and why code matters while maintaining your human focus.

# wrap up

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

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

Shito Olomide is a software engineer and technical writer passionate about leveraging modern technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. You can also get Shito Twitter.