Exploring Prompt Engineering for Developers: A Practical Guide
![Exploring Prompt Engineering for Developers: A Practical Guide](https://assets-proxy.steadyhq.com/https%3A%2F%2Fsubstackcdn.com%2Fimage%2Ffetch%2Fw_1456%2Cc_limit%2Cf_auto%2Cq_auto%3Agood%2Cfl_progressive%3Asteep%2Fhttps%253A%252F%252Fsubstack-post-media.s3.amazonaws.com%252Fpublic%252Fimages%252F11df81df-3d14-4c45-96e7-94f97aab7ee5_1600x1067.png?auto=compress&w=800&fit=max&dpr=2&fm=webp&s=7d37ab0d9c91c60c04ae352daf7f491f)
On the Internet, you can often find PDFs or websites titled “The 100 Best Prompts to Boost Your Productivity.” These prompts are mostly for tools like ChatGPT to optimize simple repetitive tasks. Most of the time, you still need to customize these prompts to your individual use case, because each use case is different. That’s why you should learn to write effective prompts yourself, especially as a software developer. That’s exactly what this article covers.
Many software developers are not yet aware of the power of Large Language Models (LLMs) in software development. Prompt engineering, in combination with production-ready LLMs, will simplify software development massively in the future. LLMs make it possible for any developer to design AI-powered applications very quickly. It’s exciting to see how LLMs have become a kind of Swiss Army knife in the development of AI applications.
In this article, we’ll show you what you can do with LLMs and how you can control LLMs with prompting. In addition, you’ll learn the best practices for developing clear and specific prompts.
We’ll discuss the following points:
Initial Setup
Types of LLMs
Approaches for Good Prompts
Conclusion
To read this post you'll need to become a member. Members help us fund our work to ensure we can stick around long-term.
See our plans (S'ouvre dans une nouvelle fenêtre)
Déjà membre ? Connexion (S'ouvre dans une nouvelle fenêtre)