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Cover image for Prompting Like a Pro – How to Talk to AI
Manoj Mishra
Manoj Mishra

Posted on • Edited on

Prompting Like a Pro – How to Talk to AI

#ai #productivity #distributedsystems #programming

The way you phrase your request determines the quality of AI’s output. Most developers treat AI like a search engine—asking vague questions and accepting the first answer. In this series post, we’ll explore five common prompting mistakes and show you how to fix them with concrete examples.

Prompting Like a Pro Infographic

Mistake 1: Vague Debugging Requests

Description: Developer provides no context while asking AI to fix an error, forcing the AI to guess.

Realistic Scenario: Production API failing after deployment. Developer pastes only the error message without any surrounding code or stack trace.

❌ Wrong Prompt:

Fix this error: "NullPointerException"
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⚠️ Why it is wrong:
No stack trace, code context, or environment details to locate the actual null reference.

✅ Better Prompt:

Fix the NullPointerException in the below Spring Boot service method.

Environment: Spring Boot 3.2, Java 17, running on Kubernetes
Error stack trace:
java.lang.NullPointerException at com.example.UserService.getUserById(UserService.java:42)
at com.example.UserController.getUser(UserController.java:18)

Code snippet:
public User getUserById(String id) {
return userRepository.findById(id).get();
}

Expected behavior: Return a default User object or throw a custom NotFoundException when user doesn't exist.
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💡 What changed: Added stack trace pinpointing exact line, code context, and expected behavior so AI can suggest proper null handling.

Mistake 2: Asking for Code Without Context

Description: Requesting implementation without providing existing codebase patterns, leading to inconsistent style and architecture.

Realistic Scenario: Junior developer asks AI to implement authentication without knowing the project uses JWT with specific custom claims format.

❌ Wrong Prompt:

Write me authentication for a Spring Boot app
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⚠️ Why it is wrong: No information about existing auth mechanism, user store, or security requirements.

✅ Better Prompt:

Implement authentication for Spring Boot 3.2 using our existing pattern.

Our codebase currently uses:

JWT with custom claims: userId, tenantId, roles

User details loaded from PostgreSQL via Spring Security

OAuth2 client for Google login

Custom AuthenticationProvider for username/password

Add a new endpoint /api/auth/login that accepts email/password, validates against database, and returns JWT with our standard claims format. Follow existing package structure under com.example.auth.
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💡 What changed: Provided existing patterns, tech stack details, and specific requirements to generate consistent code.

Mistake 3: No Iteration After First Response

Description: Accepting first AI response without iterating to refine, test, or adapt to edge cases.

Realistic Scenario: Developer needs to parse large CSV files. AI gives initial solution that works for small files but fails with memory issues in production.

❌ Wrong Prompt:

How to parse a CSV file in Python?
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Developer copies first solution without further questions.

⚠️ Why it is wrong: AI provided a basic solution that may not handle large files, encoding issues, or malformed data.

✅ Better Prompt (Iterative Approach):

Round 1: "Write Python code to parse a 5GB CSV file efficiently. Use generators and avoid loading entire file into memory."

Round 2 (after review): "Now add error handling for malformed rows, skip corrupted lines, and log errors to a file."

Round 3: "Optimize for speed using pandas with chunksize. Include progress bar for processing 10M rows."
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💡 What changed: Iterative refinement addressed scale, error handling, and performance progressively.

Mistake 4: Ignoring AI’s Clarifying Questions

Description: AI asks for clarification but developer proceeds with incomplete information, resulting in incorrect output.

Realistic Scenario: AI asks which database technology is being used for a query optimization request. Developer ignores and uses generic advice.

❌ Wrong Prompt:

Optimize this query: SELECT * FROM orders WHERE customer_id = 123
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AI: "Which database are you using?"
Developer: (ignores) "Just optimize it"

⚠️ Why it is wrong: Optimization strategies differ vastly between PostgreSQL, MySQL, MongoDB, etc. Generic advice may even degrade performance.

✅ Better Prompt:

Optimize this query for PostgreSQL 15:

SELECT * FROM orders WHERE customer_id = 123;

Current execution plan shows sequential scan on orders table (500K rows). customer_id has low cardinality (10K distinct values). Write the appropriate index and rewritten query if needed.
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💡 What changed: Answered AI's implicit questions upfront—database type, current performance, data distribution.

Mistake 5: Using Outdated Examples in Prompts

Description: Using deprecated library versions or syntax in prompts, leading to AI generating outdated code.

Realistic Scenario: Developer asks for React code using class components in 2024, missing modern patterns.

❌ Wrong Prompt:

Write a React component with state and lifecycle methods
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⚠️ Why it is wrong: AI may generate class components with deprecated lifecycle methods instead of modern hooks.

✅ Better Prompt:

Write a React 18 functional component using TypeScript that:

Fetches user data from /api/users on mount

Shows loading state

Uses React Query for caching

Handles errors with ErrorBoundary

Follows project pattern: hooks in separate files under src/hooks/
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💡 What changed: Specified modern React version, TypeScript, preferred libraries, and project structure.

Summary & Best Practices

  • Always provide context: stack traces, code snippets, environment details, expected behavior.
  • Be specific about versions and libraries: avoid ambiguous “latest” or “modern”.
  • Iterate: treat AI as a pair programmer, not a one‑shot code generator.
  • Answer AI’s questions proactively to save time.
  • Keep prompts up‑to‑date with your actual tech stack.

With these techniques, you’ll turn AI from a generic code machine into a precise, context‑aware assistant.

💬 What’s your favorite prompt trick that consistently gets better results from AI?

Drop your best examples or hacks in the comments!

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