How to Get the Most Out of Your AI Coding Assistant (Without New Tools)
- Jayant Upadhyaya
- 6 days ago
- 5 min read
A lot of people use AI coding assistants like Claude Code, Cursor, or similar tools… but they still work the old way: random prompts, scattered context, and big “do everything” requests. The result is predictable. The agent gets confused, makes messy changes, forgets constraints, and creates bugs you have to clean up later.
Top “agentic” engineers work differently. They treat AI coding like a system, not a chat. They manage context carefully, break work into small units, reuse workflows, and improve the process every time something goes wrong.
This guide explains the most useful techniques in a simple, practical way. No hype, no extra tools. Just better habits.
1) Start With PRD-First Development
PRD means Product Requirements Document. In this workflow, it’s not a long corporate document. It’s a single Markdown file that clearly defines what you’re building.
Think of it as the “one source of truth” for your project.
What goes in a PRD?
For a new project (greenfield), a PRD usually includes:
the goal of the project
who it is for (target users)
what is in scope
what is out of scope
key features
basic architecture (high-level)
For an existing project (brownfield), the PRD becomes:
what the project already does
what you want to build next
constraints and goals moving forward
Why PRD-first matters
AI agents fail when the work is vague or changes every message. A PRD solves this by giving the agent a stable “north star.”
Instead of asking the AI:
“Build the app”you do:
“Read the PRD and build feature #1”
2) Break Work Into Small Features (Don’t Ask for Too Much)
Coding agents can’t reliably build an entire product in one go. When you ask for too much at once, they:
miss details
invent features
create inconsistent structure
produce code that looks complete but is fragile
Use the PRD to split work into smaller chunks, like:
build the API endpoints
build authentication
build the UI for one screen
add logging
add error handling
write tests for one module
This keeps the agent focused and makes results more predictable.
3) “Prime” the Agent at the Start of Work
Before you start a real coding session, load the right context.
A prime step usually means:
tell the agent the project structure
point it to key files
include the PRD
include important conventions (testing, logging, commands)
This avoids the common problem where the agent wastes time guessing what’s in your codebase.
A simple daily workflow becomes:
prime the agent
ask: “Based on the PRD, what should we build next?”
plan one feature
execute that feature
4) Use a Modular Rules System (Don’t Make One Giant Rules File)
Most people write huge global rule files. That sounds smart, but it backfires.
Why? Because the agent loads those rules into context every time.
If the rules are too long, you:
waste context window space
overwhelm the model
reduce reasoning room for actual coding
Better approach: modular rules
Keep your main rule file short. Only include rules that apply all the time, such as:
how to run tests
how to run the app
logging standards
code style basics
folder structure
naming conventions
Then create separate rule docs for specific work, like:
API rules
frontend component rules
deployment rules
auth rules
database rules
Your global rules should point to these docs, but the agent should only load them when needed.
This protects the most valuable thing you have: the agent’s context window.
5) Turn Repeated Prompts Into Reusable Commands
If you type the same instruction more than twice, turn it into a reusable workflow.
This is one of the biggest “power moves” in agentic coding.
A “command” is basically a saved prompt or process description. It tells the agent exactly how to do a common task.
Examples of workflows worth commandifying:
create PRD
prime the codebase
create a structured plan
implement a feature from a plan
write a git commit message
run validation checks
do a code review checklist
Why this matters:
saves time
reduces mistakes
makes results consistent
makes your process shareable across a team
The goal is simple: fewer random prompts, more repeatable systems.
6) Do a Context Reset Between Planning and Execution
This is one of the most underrated techniques.
Most people:
plan in the same chat
keep asking questions
then immediately tell the agent to implement
The problem is that planning discussions add a lot of extra noise. By the time you get to coding, the context window is filled with:
half ideas
rejected options
long discussions
back-and-forth confusion
Better approach: two-stage workflow
Stage 1: Planning
prime the agent
decide what the next feature is
produce a structured plan in a Markdown doc
Stage 2: Execution
start a fresh conversation
feed only the structured plan
tell the agent to implement exactly what’s in the plan
This gives the agent maximum clarity and maximum “thinking room” while writing code.
It also makes your work more reliable because the plan becomes a stable input instead of a messy chat history.
7) Write a Structured Plan Document Before Coding
To make context reset work, you need a plan document that contains everything
needed for execution.
A good plan doc includes:
feature goal
user story
what files to change
what components to create
step-by-step tasks
validation checklist
testing expectations
This plan should be detailed enough that the agent can execute without needing the whole planning chat.
In other words:the plan becomes the only context the agent needs to build the feature.
8) Build in a Repeatable Feature Cycle
The workflow above becomes a repeatable cycle:
Prime (load project context + PRD)
Pick next feature (from PRD)
Plan (write structured plan doc)
Reset context
Execute (implement plan)
Validate (tests, lint, manual checks)
Improve system (rules/commands/docs)
This is how people build bigger projects with agents without chaos.
9) The Most Important Mindset: System Evolution
This is the biggest difference between casual users and serious agentic engineers.
Most people fix bugs and move on.
Agentic engineers do something smarter:
Don’t just fix the bug. Fix the system that allowed the bug.
Every bug is a signal that your process needs improvement.
Where do improvements usually go?
Most improvements fall into one of these buckets:
Global rules (short always-on rules)
Reference docs (task-specific deep guidance)
Commands/workflows (repeatable processes)
Practical examples
Problem: agent keeps using the wrong import styleFix: add a one-line rule about import style
Problem: agent forgets to run testsFix: update the plan template to include a testing section
Problem: agent keeps misunderstanding auth flowFix: create an auth reference doc and link it in global rules
Problem: agent makes the same validation mistakesFix: improve your “validation
command” workflow
This is how your agent gets better over time. Not because the AI gets smarter, but because your system becomes clearer.
10) Make the Agent Reflect After a Feature Ships
After you build and validate a feature, do a quick “process review” with the agent.
You can say something like:
“Here’s the bug we hit and how we fixed it.”
“Now check our rules, commands, and plan template.”
“What should we change so this doesn’t happen again?”
This forces the agent to compare:
what the plan said
what was built
what the rules expected
what went wrong
Over time, this creates a tight feedback loop where your workflow becomes stronger and your agent becomes more reliable.
Final Takeaway
Most people treat an AI coding assistant like a chatbot.
The best results come when you treat it like a system:
PRD-first
small feature chunks
prime the right context
modular rules
reusable commands
context reset before coding
structured plan documents
evolve the system after every bug
If you build these habits, the agent becomes less random, less fragile, and far more useful for real projects.
Comments