The short answer
Generic “write a function” prompts fail with today’s agentic AI coding tools. This guide provides 25 modern, copy-paste prompts designed for 2026’s developer workflows. They cover multi-file refactoring, security auditing, database optimization, and orchestrating agentic tools like Cursor and Claude Code, helping you get shippable results instead of plausible but flawed code.
The way developers use AI has fundamentally changed. In 2023, we prompted for boilerplate. In 2026, we direct autonomous agents that read and write across thousands of files. The old prompts no longer work. A vague request that once produced a decent function now sends an AI agent on a flawed, multi-file “improvement” spree that introduces subtle bugs.
The productivity gains from AI are real, but they now live entirely in the quality of the prompt. A well-structured prompt gives an agent the context, constraints, and success criteria needed to perform complex tasks. A weak prompt invites hallucinations and security flaws. Research from Veracode in 2026 shows that nearly 45% of AI-generated code contains security vulnerabilities when prompted without specific security guidance. This isn’t a problem with the models; it’s a problem with our instructions.
At ZEKAI, we review tools and workflows independently. We’ve spent hundreds of hours testing agentic coding tools to find the prompt structures that consistently deliver secure, production-ready code. This article is our playbook. It’s a curated library of 25 prompts built for the new era of agentic software development, covering everything from multi-file refactoring to security hardening and database tuning. These are the prompts we believe every professional on an AI software development team should have saved.
Why 2023’s Prompts Fail with 2026’s Agentic Tools
Early AI assistants were conversational partners. Today’s tools, like Cursor and Claude Code, are active collaborators with access to your entire file system, terminal, and even external tools via protocols like MCP. This shift from conversation to action is why old prompting habits are now dangerous.
code contains known security flaws when no security guidance is provided in the prompt. Source: veracode.com
There are two core problems:
- Lack of Context: A generic prompt like “Refactor the user service” doesn’t tell an agent which files matter, what the blast radius of the change is, or what “better” means. The agent is forced to guess, and it often guesses wrong.
- Iterative Degradation: Intuitively, we expect asking an AI to “improve” code repeatedly should make it better. Research shows the opposite. An IEEE-ISTAS controlled experiment found that after just five rounds of AI-assisted refinement, the number of critical vulnerabilities *increased* by 37.6%. The AI doesn’t self-correct; it often buries flaws deeper.
The prompts in this guide are designed to solve these problems. They provide explicit context, define success, and force the AI into a structured, verifiable workflow.
The Prompt Library: 25 Prompts for Agentic Workflows
We’ve organized these prompts by common developer tasks. Each is a template; replace the [bracketed] placeholders with your specific details.
Code Generation & Refactoring
These prompts move beyond generating single functions to handling complex, multi-file changes safely.
engineer specializing in large-scale refactoring. Your task is to refactor the
codebase. Before you make any changes to
. Your task is to create a migration plan for the
model Product {
id String @id @default(cuid())
name String
description String?
price Float
createdAt DateTime @default(now())
updatedAt DateTime @updatedAt
}
. I need you to first explain it, and then re-implement it in a more efficient and readable way in
Security & Code Review
AI can be a powerful security multiplier, but only if directed to be skeptical. These prompts turn the AI from an agreeable assistant into a diligent reviewer.
[Paste code snippet, e.g., a function handling user input or a database query]
file and identify potential risks.
**File Content:**
function. While the EU AI Act's Article 50 transparency obligations do not apply to source code itself, I want to adopt its principles for internal governance.
**Code Snippet:**
that needs to perform a specific task.
**Task Description:** The Lambda function needs to read objects from the
[Paste the full git diff here]
Database & SQL
AI is exceptionally good at writing and optimizing queries, but it needs to understand your schema and intent.
CREATE TABLE users (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
email VARCHAR(100) UNIQUE,
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
CREATE TABLE orders (
id SERIAL PRIMARY KEY,
user_id INTEGER REFERENCES users(id),
amount DECIMAL(10, 2),
created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);
. Analyze the following slow query and the table schema, then provide an optimized version.
**Table Schema:**
[from myapp.models import Author, Book
recent_prolific_authors = Author.objects.filter(
book__publication_date__gte='2026-01-01'
).annotate(
num_books=Count('book')
).filter(
num_books__gt=5
).order_by('-num_books')]
Testing & Debugging
Use these prompts to generate test cases, debug errors, and ensure your code is robust.
function calculateDiscount(price, quantity, userTier) {
// ... function implementation ...
}
developer debugging a production error. I will provide a stack trace and the relevant code. Your job is to identify the most likely root cause.
**Environment:**
2. **Explanation:** Break down the regex part by part in a table, explaining what each component does (e.g.,
Agentic & MCP Workflows
These prompts are for orchestrating modern, agentic tools that can interact with their environment. They often involve connecting to a Model-Controller-Personality (MCP) server, an open standard that lets an AI agent use external tools like your terminal or a database client.
VE Tool review VernLLM โ read our full review Pricing, free tier and where it falls short(for interacting with the Prisma schema and CLI)
*
(for creating GitHub issues)
**Scenario:**
The "new user registration" feature has been deployed to the staging environment at
.
3. Analyze the function signature, the Zod validation schema, and the main logic.
4. Create a new file
System Design & Architecture
Use AI as a thought partner to explore trade-offs and challenge your assumptions.
Design a system like `[Twitter's timeline service]`."
**Your Role:**
1. Start by asking me clarifying questions to scope the problem (e.g., "What are the key features?" "What is the expected scale?").
2. As I propose components (e.g., load balancers, databases, caches), challenge my decisions. Ask about trade-offs, failure modes, and scalability bottlenecks.
3. Force me to do back-of-the-envelope calculations for storage, bandwidth, and QPS.
4. Do not give me the answers. Guide me toward a robust design by asking critical questions.
5. Conclude the interview by giving me feedback on my design, highlighting 3 strengths and 3 areas for improvement.
# ADR-001: Adopt ScyllaDB as the Primary Database
**Date:** `[September 3, 2026]`
**Status:** Accepted
**Context:**
*(Describe the business/technical problem we are trying to solve.)*
**Decision:**
*(State the decision clearly.)*
**Consequences:**
* **Positive:**
* *(List the expected benefits, e.g., improved write throughput, lower read latency, horizontal scalability.)*
* **Negative:**
* *(List the downsides and risks, e.g., loss of ACID transactions, need for new developer skills, migration complexity.)*
**Options Considered:**
1. **ScyllaDB:** *(Briefly describe why this was chosen.)*
2. **PostgreSQL with CitusDB:** *(Briefly describe why this was not chosen.)*
3. **CockroachDB:** *(Briefly describe why this was not chosen.)*
[a real-time chat application for large online communities]
Miscellaneous Productivity Prompts
to me.
**Instructions:**
* Assume I am an experienced developer who has only ever worked with garbage-collected languages like
[I accidentally committed a large binary file to my feature branch. I have made several other commits on top of it. I want to remove that file and its commit from my branch history before I create a pull request. I have not pushed the branch to the remote yet.]
/
โโโ app/
โ โโโ layout.tsx
โ โโโ page.tsx
โโโ components/
โ โโโ ui/
โโโ lib/
โ โโโ utils.ts
โโโ public/
โโโ package.json
โโโ tsconfig.json
โโโ next.config.mjs
โโโ tailwind.config.ts
Where to go next
Three routes, picked for what you just read.
How do you write a good AI prompt for coding?
A good coding prompt is specific and context-rich. It should define a persona for the AI (e.g., “Act as a senior Go developer”), provide the relevant code or schema, state the goal clearly, and specify the desired output format. Avoid vague requests like “fix my code.”
Can ChatGPT write an entire program?
Yes, for simple programs, ChatGPT and other advanced models can write complete, functional applications from a single prompt. However, for complex software, it’s more effective to use AI to generate components, write tests, or refactor specific parts of a larger, human-architected system. The risk of architectural flaws increases with project size.
What is the best AI for writing code?
As of September 2026, the “best” AI depends on the task. For IDE-native code completion, GitHub Copilot is a strong choice. For agentic, multi-file tasks and refactoring, tools like Cursor and Claude Code are often more powerful because they have deeper integration with the developer’s environment.
How do I get ChatGPT to review my code?
To get a useful code review, provide a specific prompt. Assign a persona (e.g., “Act as a skeptical security engineer”), paste the code, and state your goal. Ask it to check for specific vulnerability classes (like OWASP Top 10) or to enforce a particular style guide, rather than just asking if the code “looks good.”
Are AI-generated prompts better?
It depends. AI can generate prompt *templates*, but a human must provide the specific context (code, schema, intent) to make the prompt effective. Using an AI to help you brainstorm different angles for a prompt (e.g., “Give me 5 ways to ask an AI to critique this function”) can be a very powerful meta-workflow.
Where to go next
Three routes, picked for what you just read.
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