If you've spent any time with tools like ChatGPT or GitHub Copilot, you know that getting them to produce some code is easy. But moving from generating simple, isolated functions to creating reliable, production-quality code that fits into a larger system requires a deeper level of skill. This is the discipline of prompt engineering, and it's rapidly becoming a core competency for the modern developer.
Basic prompting is just giving the model a simple instruction. Advanced prompting is about controlling the model's output with precision, guiding its "thought process," and giving it the context it needs to perform like a senior developer. Let's move beyond the basics.
1. The Persona Pattern: Prime the Model for Success
The first and simplest step to getting better output is to tell the model who it should be. Before you even ask it to do something, give it a role. This primes the model's virtual "cortex," focusing it on the specific domain and patterns relevant to your request.
Basic Prompt:
> "Write a VB.NET function that takes a connection string and a SQL query and returns a DataTable."
Advanced Prompt (with Persona):
> "You are an expert .NET developer with 20 years of experience specializing in writing clean, performant, and secure data access code for enterprise applications. Your primary language is VB.NET.
>
> Write a robust VB.NET function that takes a connection string and a SQL query and returns a DataTable. Ensure you use `Using` blocks to properly dispose of all `IDisposable` objects, and include error handling for common `SqlException` scenarios."
The second prompt will consistently produce higher-quality, more complete, and safer code because you've framed the request within a context of expertise.
2. Chain-of-Thought (CoT) Prompting: Force the Model to Think
LLMs often fail on complex tasks because they try to generate the answer in one go. Chain-of-Thought (CoT) prompting forces the model to slow down and "think step by step." By instructing it to outline its logic before providing the final answer, you dramatically increase the likelihood of a correct result and make it easier to debug where it went wrong.
Basic Prompt:
> "Given a list of user objects with properties `Name` and `SignupDate`, and a list of `Role` objects with properties `RoleName` and `AssignedUsers` (a list of user names), return a list of users who signed up in the last 30 days and have the 'Admin' role."
Advanced Prompt (with CoT):
> "You need to return a list of users who signed up in the last 30 days and have the 'Admin' role.
>
> First, think step by step:
> 1. Identify the 'Admin' role from the list of roles.
> 2. Get the list of user names assigned to the 'Admin' role.
> 3. Filter the main list of users to find those whose `SignupDate` is within the last 30 days.
> 4. From that filtered list, find the users whose `Name` is present in the list of admin user names.
> 5. Return this final list of user objects.
>
> Now, based on these steps, write the VB.NET code to accomplish this."
By forcing the model to lay out its plan, you've created a logical scaffolding for it to build the code on, reducing the chance of it missing a step.
3. Few-Shot Prompting: Show, Don't Just Tell
Sometimes, the most important thing is getting the output in a specific, predictable format (e.g., JSON, XML, or a particular coding style). "Few-shot" prompting is the technique of providing examples of inputs and their corresponding desired outputs directly in the prompt. The model uses these examples as a template.
Advanced Prompt (with Few-Shot example for JSON):
> "You are a text processing engine that extracts key information from user feedback and returns it as a structured JSON object.
>
> **Example 1:**
> **Input:** 'I love the new dashboard, but the login button is broken on Firefox.'
> **Output:**
> ```json
> {
> "sentiment": "mixed",
> "feature": "dashboard",
> "bug_report": "login button broken on Firefox"
> }
> ```
>
> **Example 2:**
> **Input:** 'The new reporting feature is amazing! So much better.'
> **Output:**
> ```json
> {
> "sentiment": "positive",
> "feature": "reporting",
> "bug_report": null
> }
> ```
>
> Now, process the following input:
> **Input:** 'Everything seems to be working fine, but the app feels a bit slow on startup.'
> **Output:**"
The model will now almost certainly return a correctly formatted JSON object because you've shown it exactly what you want.
Conclusion
Prompting is not just about asking questions; it's a new form of programming. By mastering these advanced techniques, you can elevate AI from a fun toy to a serious, industrial-strength development tool. The key is to shift your mindset from being a simple instruction-giver to being a meticulous architect of context.
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Further Reading
1. OpenAI - Prompt engineering guide
Description: Official guide from OpenAI covering best practices, common techniques like few-shot and chain-of-thought, and general principles for effective prompting.
2. Google AI - Prompt Engineering Guide
A comprehensive guide from Google AI that delves into various prompting strategies, including different types of few-shot, chain-of-thought, and advanced techniques for complex tasks.
3. Lil'Log - Chain of Thought Prompting
A detailed overview of Chain-of-Thought prompting, explaining its mechanisms, variants, and why it's effective for improving LLM reasoning.
4. Prompt Engineering Guide (Learn Prompting)
A community-driven, open-source project that serves as a comprehensive resource for all things prompt engineering, offering tutorials, techniques, and real-world examples.
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