ChatGPT Prompt Engineering for Developers

#promptengineering #chatgpt #deeplearning

Course: ChatGPT Prompt Engineering for Developers
Source: DeepLearning.AI Course
Instructors: Andrew Ng (DeepLearning.AI) & Isa Fulford (OpenAI)

Why Should Developers Care About Prompt Engineering?
AI is changing how we code, build products, and even debug. If you’re an engineer looking to level up your LLM application game—or just tired of getting inconsistent results from ChatGPT—learning prompt engineering is essential.

This post condenses the comprehensive DeepLearning.AI course into actionable, developer-ready best practices you can apply right away, with real-world coding and product examples.

Table of Contents
Understanding LLM Types

Core Prompting Philosophy

Best Practices Checklist

Iterative Prompt Development

Summarization Techniques

Inferring Insights

Text Transformation

Content Expansion

Building Chatbots

Your Next Steps

Understanding LLM Types
Base LLMs:
Predict next-word sequences; may not follow instructions.

Instruction-tuned LLMs:
Specifically trained to follow instructions. Always prefer these for practical use—think gpt-3.5-turbo, gpt-4, etc.

Core Prompt Engineering Philosophy
Treat LLMs as highly skilled collaborators who lack your context—be explicit, be clear, and always check your instructions.

Best Practices (Checklist for Devs!)
Write clear, specific instructions

Use delimiters (e.g. triple backticks) to separate user content

Request structured output (JSON/HTML)

Instruct the model to check preconditions

Provide examples (few-shot prompting)

Chain-of-thought reasoning for complex tasks

Specify output format/order

Ask for reasoning before answers

Validate critical outputs (watch for hallucinations)

Ask for citations/quotes in summaries

More Developer-Focused Sample Section
Iterative Prompt Development: The Dev Way
Start with a simple prompt.

Analyze the output.

Refine requirements: add structure, word limits, or technical requirements.

Automate testing: For production, validate against real data and edge cases.

Reminder: “Perfect” prompts rarely exist—iteration is success.

Practical Prompts: Real Use Cases
json
// Ask for structured, machine-readable output
{
"sentiment": "positive",
"emotion": "joy",
"main_topic": "product feedback"
}
text

Example: Summarize Product Reviews

Prompt:
Extract the main feature request and overall sentiment from the following review:

Output as JSON.

Your Next Steps as a Developer
Experiment: Try integrating LLMs into your projects.

Document: Keep a log of prompt outcomes (helps rapid improvements).

Iterate: Small tweaks, big results.

Share: Comment with your own prompt hacks below.

Keep Learning: New LLM capabilities land regularly—stay curious.

About This Guide:
Summarized from the DeepLearning.AI course by Andrew Ng and Isa Fulford. Highly recommend taking the course for direct hands-on practice.
Course Link: https://learn.deeplearning.ai/courses/chatgpt-prompt-eng/