ChatGPT prompt engineering for developers
ProductA short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).
Capabilities9 decomposed
prompt structure decomposition and pattern teaching
Medium confidenceTeaches developers systematic frameworks for constructing prompts through guided examples and iterative refinement patterns. The course breaks down prompt engineering into discrete components (instructions, context, examples, output format specifications) and demonstrates how each component affects model behavior through live API interactions with GPT models, enabling developers to understand the causal relationship between prompt design choices and output quality.
Authored by Isa Fulford from OpenAI and Andrew Ng, providing insider perspective on how GPT models interpret prompts; uses live API demonstration methodology rather than theoretical lectures, showing real model outputs for each prompt variation to build intuition about prompt-behavior relationships
Provides authoritative, model-creator-endorsed prompt engineering methodology backed by live demonstrations, whereas most alternatives rely on crowdsourced examples or theoretical frameworks without direct OpenAI engineering input
iterative prompt refinement methodology
Medium confidenceTeaches a systematic approach to prompt improvement through hypothesis-driven iteration: define success criteria, test a prompt variant, analyze output quality against criteria, identify failure modes, and refactor the prompt based on root cause analysis. The course demonstrates this cycle through concrete examples where prompts are progressively refined to handle edge cases, reduce hallucination, and improve output structure, building developer intuition for debugging prompt behavior.
Frames prompt engineering as a scientific debugging process with explicit hypothesis formation and testing, rather than trial-and-error; demonstrates how to read model outputs to infer what the model misunderstood about the prompt, enabling targeted fixes
Teaches the underlying reasoning process for prompt improvement rather than just providing prompt templates, enabling developers to solve novel problems rather than copying existing examples
prompt design pattern library with task-specific examples
Medium confidenceProvides a curated collection of prompt design patterns (e.g., few-shot learning, chain-of-thought, role-based prompting, output format specification) with concrete, runnable examples for common developer tasks like text summarization, sentiment analysis, content generation, and code explanation. Each pattern is demonstrated with multiple variants showing how parameter changes affect output, enabling developers to recognize which pattern applies to their specific use case.
Patterns are taught through live API demonstrations showing exact input-output pairs, allowing developers to see precisely how prompt variations change model behavior rather than reading abstract descriptions
Provides authoritative patterns from OpenAI engineers with demonstrated effectiveness on GPT models, whereas community prompt libraries often lack validation or explanation of why patterns work
hallucination reduction and factual grounding techniques
Medium confidenceTeaches specific prompt engineering techniques to reduce model hallucination and improve factual accuracy, including: instructing models to cite sources, asking models to reason before answering, constraining outputs to provided context, and using explicit 'I don't know' instructions. The course demonstrates how these techniques work through examples where the same task is prompted different ways, showing measurable differences in hallucination rates and output reliability.
Demonstrates hallucination reduction as a prompt design problem rather than a model limitation, showing how specific instruction patterns and output constraints measurably reduce false outputs without requiring model retraining or fine-tuning
Provides practical, immediately applicable techniques for reducing hallucination through prompting, whereas academic approaches often focus on model-level solutions or post-hoc filtering
structured output format specification and parsing
Medium confidenceTeaches how to design prompts that produce machine-parseable structured outputs (JSON, XML, CSV, markdown tables) by explicitly specifying output format requirements, providing format examples, and constraining the model's response structure. The course demonstrates how format specification affects model compliance and shows techniques for handling cases where models deviate from specified formats, enabling developers to reliably extract structured data from model outputs.
Teaches output format specification as a core prompt engineering technique with explicit examples of format templates and compliance strategies, rather than treating structured output as a secondary concern or relying on post-processing
Provides practical guidance on achieving reliable structured outputs through prompting, whereas alternatives often require external tools like JSON schema validators or custom parsing logic to handle model deviations
few-shot learning prompt construction
Medium confidenceTeaches how to construct few-shot prompts by selecting and formatting representative examples that guide model behavior toward desired outputs. The course demonstrates how example selection, ordering, and formatting affect model performance, and shows techniques for identifying when few-shot learning is necessary versus when zero-shot prompting suffices. Developers learn to recognize patterns in their task that benefit from examples and how to structure those examples for maximum effectiveness.
Teaches few-shot learning as a deliberate prompt engineering technique with explicit guidance on example selection, ordering, and formatting, rather than treating it as an obvious best practice; demonstrates how example quality and relevance directly impact model behavior
Provides systematic guidance on constructing effective few-shot prompts, whereas most resources assume developers already know how to select and format examples
role-based and persona prompting
Medium confidenceTeaches how to assign roles or personas to language models through prompting (e.g., 'You are an expert Python developer' or 'You are a customer service representative') and how this affects model behavior, output style, and domain expertise. The course demonstrates through examples how role specification influences the model's knowledge access, reasoning patterns, and communication style, enabling developers to tailor model outputs to specific contexts or audiences.
Demonstrates role-based prompting as a deliberate technique for controlling model behavior and expertise, with examples showing how different roles produce measurably different outputs for the same task
Provides concrete examples of role-based prompting effectiveness, whereas most resources mention it casually without demonstrating its impact on output quality or style
chain-of-thought prompting for complex reasoning
Medium confidenceTeaches how to structure prompts to encourage step-by-step reasoning before final answers (chain-of-thought), improving model performance on complex tasks like math, logic, and multi-step problem solving. The course demonstrates how explicitly asking the model to 'think through' problems or 'show your work' leads to more accurate and verifiable outputs, and shows techniques for formatting reasoning chains for clarity and debuggability.
Demonstrates chain-of-thought as a prompt engineering technique that measurably improves reasoning accuracy, with examples showing how the same task produces different quality outputs with and without explicit reasoning instructions
Provides practical guidance on implementing chain-of-thought prompting, whereas academic literature focuses on the theoretical benefits without practical implementation guidance
api integration patterns for production llm applications
Medium confidenceTeaches practical patterns for integrating OpenAI APIs into production applications, including error handling, rate limiting, cost optimization, and managing API responses. The course demonstrates how to structure code for reliable API calls, handle edge cases like timeouts or quota limits, and design applications that gracefully degrade when API calls fail. Developers learn to think about LLM integration as a systems problem, not just a prompt problem.
Teaches API integration as a core part of prompt engineering rather than a separate concern, demonstrating how prompt design decisions affect API costs, latency, and reliability
Provides practical production patterns from OpenAI engineers, whereas most prompt engineering resources focus only on prompt design without addressing integration challenges
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Prompt Engineering for ChatGPT - Vanderbilt University
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** (Source: https://github.com/f/prompts.chat/tree/main/src/content/book)
Prompt Engineering Guide
Guide and resources for prompt...
Learn Prompting
A free, open source course on communicating with artificial intelligence.
Promptmetheus
ChatGPT prompt engineering...
Best For
- ✓Developers building LLM-powered applications who need systematic prompt design methodology
- ✓Teams transitioning from basic prompt-and-pray approaches to structured prompt engineering
- ✓Engineers integrating GPT APIs into production systems who need to optimize prompt quality
- ✓Individual developers prototyping LLM features who need to rapidly iterate on prompt quality
- ✓Teams building production systems where prompt quality directly impacts user experience
- ✓Non-technical product managers who need to understand how prompt changes affect model behavior
- ✓Developers building their first LLM features who need working starting points
- ✓Teams standardizing on prompt patterns across multiple applications
Known Limitations
- ⚠Course content is static and may not reflect latest GPT model capabilities or API changes
- ⚠Examples are primarily focused on GPT-3.5 and GPT-4; patterns may not transfer equally to other model families
- ⚠No hands-on environment provided — requires learners to set up their own OpenAI API access and Python environment
- ⚠Limited coverage of advanced techniques like chain-of-thought prompting at scale or multi-turn conversation state management
- ⚠Methodology is qualitative and experience-dependent; no quantitative metrics or automated evaluation frameworks provided
- ⚠Does not cover systematic A/B testing or statistical significance testing for prompt variants
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A short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).
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