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# Introduction
Most builders don’t need assistance typing quicker. What slows initiatives down are the countless loops of setup, assessment, and rework. That’s the place AI is beginning to make an actual distinction.
Over the previous 12 months, instruments like GitHub Copilot, Claude, and Google’s Jules have developed from autocomplete assistants into coding brokers that may plan, construct, check, and even assessment code asynchronously. As a substitute of ready so that you can drive each step, they will now act on directions, clarify their reasoning, and push working code again to your repo.
The shift is refined however vital: AI is not simply serving to you write code; it’s studying how you can work alongside you. With the suitable method, these programs can save hours in your day by dealing with the repetitive, mechanical points of improvement, permitting you to deal with structure, logic, and choices that actually require human judgment.
On this article, we’ll look at 5 AI-assisted coding methods that save vital time with out compromising high quality, starting from feeding design paperwork immediately into fashions to pairing two AIs as coder and reviewer. Each is straightforward sufficient to undertake right now, and collectively they kind a better, quicker improvement workflow.
# Approach 1: Letting AI Learn Your Design Docs Earlier than You Code
One of many best methods to get higher outcomes from coding fashions is to cease giving them remoted prompts and begin giving them context. While you share your design doc, structure overview, or function specification earlier than asking for code, you give the mannequin an entire image of what you’re making an attempt to construct.
For instance, as a substitute of this:
# weak immediate
"Write a FastAPI endpoint for creating new customers."
strive one thing like this:
# context-rich immediate
"""
You are serving to implement the 'Person Administration' module described under.
The system makes use of JWT for auth, and a PostgreSQL database by way of SQLAlchemy.
Create a FastAPI endpoint for creating new customers, validating enter, and returning a token.
"""
When a mannequin “reads” design context first, its responses turn out to be extra aligned together with your structure, naming conventions, and information move.
You spend much less time rewriting or debugging mismatched code and extra time integrating.
Instruments like Google Jules and Anthropic Claude deal with this naturally; they will ingest Markdown, system docs, or AGENTS.md recordsdata and use that information throughout duties.
# Approach 2: Utilizing One to Code, One to Assessment
Each skilled group has two core roles: the builder and the reviewer. Now you can reproduce that sample with two cooperating AI fashions.
One mannequin (for instance, Claude 3.5 Sonnet) can act because the code generator, producing the preliminary implementation primarily based in your spec. A second mannequin (say, Gemini 2.5 Professional or GPT-4o) then opinions the diff, provides inline feedback, and suggests corrections or exams.
Instance workflow in Python pseudocode:
code = coder_model.generate("Implement a caching layer with Redis.")
assessment = reviewer_model.generate(
f"Assessment the next code for efficiency, readability, and edge circumstances:n{code}"
)
print(assessment)
This sample has turn out to be frequent in multi-agent frameworks reminiscent of AutoGen or CrewAI, and it’s constructed immediately into Jules, which permits an agent to put in writing code and one other to confirm it earlier than making a pull request.
Why does it save time?
- The mannequin finds its personal logical errors
- Assessment suggestions comes immediately, so that you merge with increased confidence
- It reduces human assessment overhead, particularly for routine or boilerplate updates
# Approach 3: Automating Exams and Validation with AI Brokers
Writing exams isn’t arduous; it’s simply tedious. That’s why it’s top-of-the-line areas to delegate to AI. Trendy coding brokers can now learn your current check suite, infer lacking protection, and generate new exams robotically.
In Google Jules, for instance, as soon as it finishes implementing a function, it runs your setup script inside a safe cloud VM, detects check frameworks like pytest or Jest, after which provides or repairs failing exams earlier than making a pull request.
Right here’s what that workflow may seem like conceptually:
# Step 1: Run exams in Jules or your native AI agent
jules run "Add exams for parseQueryString in utils.js"
# Step 2: Assessment the plan
# Jules will present the recordsdata to be up to date, the check construction, and reasoning
# Step 3: Approve and watch for check validation
# The agent runs pytest, validates adjustments, and commits working code
Different instruments also can analyze your repository construction, establish edge circumstances, and generate high-quality unit or integration exams in a single cross.
The largest time financial savings come not from writing brand-new exams, however from letting the mannequin repair failing ones throughout model bumps or refactors. It’s the form of sluggish, repetitive debugging job that AI brokers deal with persistently nicely.
In observe:
- Your CI pipeline stays inexperienced with minimal human consideration
- Exams keep updated as your code evolves
- You catch regressions early, while not having to manually rewrite exams
# Approach 4: Utilizing AI to Refactor and Modernize Legacy Code
Previous codebases sluggish everybody down, not as a result of they’re unhealthy, however as a result of nobody remembers why issues have been written that means. AI-assisted refactoring can bridge that hole by studying, understanding, and modernizing code safely and incrementally.
Instruments like Google Jules and GitHub Copilot actually excel right here. You may ask them to improve dependencies, rewrite modules in a more recent framework, or convert courses to capabilities with out breaking the unique logic.
For instance, Jules can take a request like this:
"Improve this undertaking from React 17 to React 19, undertake the brand new app listing construction, and guarantee exams nonetheless cross."
Behind the scenes, here’s what it does:
- Clones your repo right into a safe cloud VM
- Runs your setup script (to put in dependencies)
- Generates a plan and diff exhibiting all adjustments
- Runs your check suite to verify the improve labored
- Pushes a pull request with verified adjustments
# Approach 5: Producing and Explaining Code in Parallel (Async Workflows)
While you’re deep in a coding dash, ready for mannequin replies can break your move. Trendy agentic instruments now assist asynchronous workflows, letting you offload a number of coding or documentation duties directly whereas staying targeted in your essential work.
Think about this utilizing Google Jules:
# Create a number of AI coding periods in parallel
jules distant new --repo . --session "Write TypeScript varieties for API responses"
jules distant new --repo . --session "Add enter validation to /signup route"
jules distant new --repo . --session "Doc auth middleware with docstrings"
You may then preserve working regionally whereas Jules runs these duties on safe cloud VMs, opinions outcomes, and stories again when executed. Every job will get its personal department and plan so that you can approve, which means you’ll be able to handle your “AI teammates” like actual collaborators.
This asynchronous, multi-session method saves huge time in distributed groups:
- You may queue up 3–15 duties (relying in your Jules plan)
- Outcomes arrive incrementally, so nothing blocks your workflow
- You may assessment diffs, settle for PRs, or rerun failed duties independently
Gemini 2.5 Professional, the mannequin powering Jules, is optimized for long-context, multi-step reasoning, so it doesn’t simply generate code; it retains monitor of prior steps, understands dependencies, and syncs progress between duties.
# Placing It All Collectively
Every of those 5 methods works nicely by itself, however the actual benefit comes from chaining them right into a steady, feedback-driven workflow. Right here’s what that would seem like in observe:
- Design-driven prompting: Begin with a well-structured spec or design doc. Feed it to your coding agent as context so it is aware of your structure, patterns, and constraints.
- Twin-agent coding loop: Run two fashions in tandem, one acts because the coder, the opposite because the reviewer. The coder generates diffs or pull requests, whereas the reviewer runs validation, suggests enhancements, or flags inconsistencies.
- Automated check and validation: Let your AI agent create or restore exams as quickly as new code lands. This ensures each change stays verifiable and prepared for CI/CD integration.
- AI-driven refactoring and upkeep: Use asynchronous brokers like Jules to deal with repetitive upgrades (dependency bumps, config migrations, deprecated API rewrites) within the background.
- Immediate evolution: Feed again outcomes from earlier duties — successes and errors alike — to refine your prompts over time. That is how AI workflows mature into semi-autonomous programs.
Right here’s a easy high-level move:
Picture by WriterEvery agent (or mannequin) handles a layer of abstraction, preserving your human consideration on why the code issues
# Wrapping Up
AI-assisted improvement isn’t about writing code for you. It’s about releasing you to deal with structure, creativity, and drawback framing, the components no AI or machine can substitute.
When you use these instruments thoughtfully, these instruments flip hours of boilerplate and refactoring into strong codebases, whereas providing you with house to suppose deeply and construct deliberately. Whether or not it’s Jules dealing with your GitHub PRs, Copilot suggesting context-aware capabilities, or a customized Gemini agent reviewing code, the sample is similar.
Shittu Olumide is a software program engineer and technical author captivated with leveraging cutting-edge applied sciences to craft compelling narratives, with a eager eye for element and a knack for simplifying complicated ideas. You can too discover Shittu on Twitter.
