How ChatGPT, Claude, and Perplexity AI help developers master prompt engineering
In 2025, writing a great prompt is as essential to a developer’s workflow as knowing your primary programming language. Once I started running ChatGPT software, the Claude language model, and the Perplexity AI company together, I saw how much of a difference precision, sequencing, and framing make in getting the right output the first time.
Prompt engineering isn’t just about typing requests into an AI — it’s about structuring them so the model works like a senior teammate who knows exactly what you want. The following seven tricks are the same ones I’ve used to speed up builds, debug cleaner, and deliver features faster than ever.
Prompt Engineering Trick #1 – Define output format before asking the question
Developers waste time when they have to reformat AI output. Before you even ask, specify the structure — JSON, YAML, markdown, or plain text — so the AI delivers something you can use directly.
Example:
“Provide the API endpoint documentation in JSON format with keys for endpoint, method, parameters, and example_response.”
Prompt Engineering Trick #2 – Give examples of both good and bad output
AI learns your standards faster if you show contrast. Include a “don’t do this” example next to your desired style to prevent lazy or verbose responses.
Example:
“Good output: concise, single-function Python code with docstrings. Bad output: multi-function example with unused imports and no comments.”
Prompt Engineering Trick #3 – Layer prompts for complex builds
Instead of one mega-prompt, break a project into smaller requests and feed the AI’s answers back in sequence. This reduces hallucinations and keeps logic consistent across components.
Workflow:
- Generate database schema.
- Use schema to create backend endpoints.
- Connect endpoints to a front-end scaffold.
Prompt Engineering Trick #4 – Ask for multiple solutions, then merge
When debugging or designing, get the AI to give you several approaches so you can compare trade-offs before coding.
Example:
“Give me three alternative ways to implement JWT authentication in a Node.js API, each with pros, cons, and sample code.”
Prompt Engineering Trick #5 – Use role prompts for technical context
Framing the AI as a specific type of engineer produces more relevant outputs. “Act as a senior backend engineer” gives better answers than a generic request.
Example:
“You are a senior backend engineer with 10 years of Node.js experience. Suggest a scalable architecture for handling 100K concurrent WebSocket connections.
Prompt Engineering Trick #6 – Force edge-case handling
A lot of code from AI fails because it’s only designed for the happy path. Include a requirement to cover failure modes and unusual input cases.
Example:
“Write a TypeScript function to parse CSV uploads. Include handling for malformed rows, empty fields, and invalid UTF-8 characters.”
Prompt Engineering Trick #7 – Include performance constraints
When speed or memory use matters, state the limits in your prompt so the AI optimizes from the start.
Example:
“Generate a Python script to process 1M JSON records in under 10 minutes on a 4-core CPU with 8GB RAM.”
How Chatronix supercharges these 7 tricks for developers
After moving my prompt engineering workflow into Chatronix, the efficiency jumped again.
Here’s why developers benefit most:
- Six models at once – See how different AIs handle the same technical request.
- Turbo mode – All six deliver in seconds, perfect for iterative dev cycles.
- One Perfect Answer – Merges the strongest parts of each output into a single, production-ready result.
- No cross-model drift – Every model sees the same detailed brief.
| Chatronix Feature | Without Chatronix | With Chatronix + One Perfect Answer |
| Number of solutions | 1 | 6 |
| Merge quality | Manual, time-consuming | Automated, optimized |
| Turnaround time | Minutes | Seconds |
| Code consistency | Variable | Stable |
With One Perfect Answer, I no longer waste time cherry-picking between outputs — the merged version already has the cleanest logic, best formatting, and the most robust edge-case handling.
Run multiple AI models in one place with Chatronix and see how it changes your build speed.
Developer-focused prompts to apply these tricks today
- Format-first code generation
“Write a React component in JSX format with inline CSS as a JSON object.”
- Good vs bad contrast
“Good: minimal, DRY code. Bad: repetitive functions and inline SQL queries.”
- Layered build sequence
“Step 1: Generate PostgreSQL schema. Step 2: Create Express.js API based on schema.”
- Multi-option approach
“Provide three methods for caching API responses in Redis, with trade-offs.”
- Role-based context
“As a senior DevOps engineer, create a Terraform config for AWS with autoscaling EC2 instances.”
- Edge-case coverage
“Write a Go program that processes uploaded images, including corrupt files and unsupported formats.”
- Performance constraint
“Optimize a MySQL query to return results from a 10M row table in under 200ms.”
Bonus – Prompt Engineering Sprint Kit
Here’s a compact, developer-ready kit to try in your own projects:
- Architecture Design
“Act as a solutions architect. Design a microservices architecture for a fintech app, with diagrams in ASCII.”
- Code Optimization
“Refactor this Python code for better memory use, keeping runtime under 5 seconds.”
- Test Coverage
“Generate unit and integration tests for this API in Jest.”
- Security Review
“Analyze this code for security vulnerabilities and suggest fixes.”
- Documentation Output
“Write a README.md with installation, usage, and contribution guidelines.”
Run this kit in Chatronix with Turbo and One Perfect Answer — you’ll get outputs from six top models, combined into one best-version result you can drop directly into your project.
I compiled a prompt engineering “best practices and tricks” doc 😀
Created based on OpenAI @isafulf‘s prompt engineering talk at @NeurIPSConf and enriched with more details, examples, and tips.
I focused on making the document as comprehensive and concise as possible, and it… pic.twitter.com/nPjux6JSzO
— Sarah Chieng (@SarahChieng) January 1, 2024
Why mastering prompt engineering matters for developers in 2025
With the right prompts, AI stops being a “helpful assistant” and becomes a core development tool — one that speeds up prototyping, automates testing, and handles repetitive coding tasks so you can focus on architecture and innovation.
Prompt engineering isn’t going away. In fact, the developers who know how to guide AI effectively will outpace everyone else.
