Rules to Better AI Development

"Vibe coding" is a trend that has taken the software development world by storm recently. It means developing via a coding agent, and never even looking at - let alone editing - the code. It has also become synonymous with low-quality code 👎.

When writing code as a professional developer, "vibe coding" may make it easy to get a solution up and running without worrying about the details, but as soon as you commit it to the repository under your name, it becomes your responsibility, as if you had written it yourself.

Vibe coding empowers non-developers to build software using AI agents. However, without proper foundation and structure, vibe coding can result in unmaintainable code that fails "the bus test" – meaning if you were hit by a bus tomorrow, no one else could understand or maintain your code.

GitHub Copilot CLI is incredibly powerful, but giving AI deep access to your terminal and file system can be concerning. When you use features like --allow-all-tools - which approves all actions - Copilot can execute commands on your behalf, which means one wrong suggestion could have serious consequences.

Running Copilot CLI in a secure Docker container provides the best of both worlds: powerful AI assistance with strict security boundaries that limit the "blast radius" of any potential mistakes.

Previously, testing desktop features created with AI agents meant checking out a PR branch locally, building the app, and running it manually. Which took time, slowed feedback loops, and encouraged "vibe coding" where changes are shipped without a deep understanding of the code.

By exposing a settings option to switch to specific PR builds, they can be easily installed tested - no local branch juggling or manual builds required.

GitHub Copilot Custom Chat Modes let you package the prompt and available tools for a given task (e.g. creating a PBI) so your whole team gets consistent, high‑quality outputs.

Without a chat mode, individuals might copy/paste prompts . Important acceptance criteria or governance links get lost. New starters don't know the “standard way” and quality varies.

The advent of GPT and LLMs have sent many industries for a loop. If you've been automating tasks with ChatGPT, how can you share the efficiency with others?

GPT is an awesome product that can do a lot out-of-the-box. However, sometimes that out-of-the-box model doesn't do what you need it to do.

In that case, you need to provide the model with more training data, which can be done in a couple of ways.

When you're building a custom AI application using a GPT API you'll probably want the model to respond in a way that fits your application or company. You can achieve this using the system prompt.

AI agents are autonomous entities powered by AI that can perform tasks, make decisions, and collaborate with other agents. Unlike traditional single-prompt LLM interactions, agents act as specialized workers with distinct roles, tools, and objectives.

Repetitive tasks like updating spreadsheets, sending reminders, and syncing data between services are time-consuming and distract your team from higher-value work. Businesses that fail to automate these tasks fall behind.

The goal is to move from humans doing and approving the work, to automation doing and humans approving the work.

There's lots of awesome AI tools being released, but combining these can become very hard as an application scales. Semantic Kernel can solve this problem by orchestrating all our AI services for us.

When using Azure AI services, you often choose between Small Language Models (SLMs) and powerful cloud-based Large Language Models (LLMs), like Azure OpenAI. While Azure OpenAI offer significant capabilities, they can also be expensive. In many cases, SLMs like Phi-3, can perform just as well for certain tasks, making them a more cost-effective solution. Evaluating the performance of SLMs against Azure OpenAI services is essential for balancing cost and performance.

When building an AI-powered solution, developers will inevitably need to choose which Large Language Model (LLM) to use. Many powerful models exist (Llama, GPT, Gemini, Mistral, Grok, DeepSeek, etc.), and they are always changing and subject to varying levels of news and hype.

When choosing one for a project, it can be hard to know which to pick, and if you're making the right choice - being wrong could cost valuable performance and UX points.

Because different LLMs are good at different things, it's essential to test them on your specific use case to find which is the best.

When integrating Azure AI's language models (LLMs) into your application, it’s important to ensure that the responses generated by the LLM are reliable and consistent. However, LLMs are non-deterministic, meaning the same prompt may not always generate the exact same response. This can introduce challenges in maintaining the quality of outputs in production environments. Writing integration tests for the most common LLM prompts helps you identify when model changes or updates could impact your application’s performance.

A chatbot is a computer program that uses artificial intelligence to engage in text or voice conversations with users, often to answer questions, provide assistance, or automate tasks. In the age of generative AI, good chatbots have become a necessary part of the user experience.

✅ Figure: Good example - A nice chatbot in action

Choosing the right chatbot service for your website can be a challenging task. With so many options available it's essential to find the one that best fits your needs and provides a good experience for your users. But what distinguishes a good chatbot from a great one? Here are some factors to consider.

ChatGPT has an awesome API and Azure services that you can easily wire into any app.

The ChatGPT API is a versatile tool capable of far more than just facilitating chat-based conversations. By integrating it into your own applications, it can provide diverse functionalities in various domains. Here are some creative examples of how you might put it to use:

Embedding a user interface (UI) into an AI chat can significantly enhance user interaction, making the chat experience more dynamic and user-friendly. By incorporating UI elements like buttons, forms, and multimedia, you can streamline the conversation flow and improve user engagement.

Comparing and classifying text can be a very time-consuming process, especially when dealing with large volumes of data. However, did you know that you can streamline this process using embeddings?

By leveraging embeddings, you can efficiently compare, categorize, and even cluster text based on their underlying meanings, making your text analysis not only faster but also more accurate and insightful. Whether you're working with simple keyword matching or complex natural language processing tasks, embeddings can revolutionize the way you handle textual data.

Since the release of GitHub Copilot, we have witnessed a dramatic evolution in how developers work within their IDE. It started with a simple AI autocomplete, then a chat function, and now we are in an agentic gold mine. AI has now been integrated deeply into IDEs with products like Visual Studio Code and Cursor, embedding an even deeper level of AI integration within a developer's workflow.

AI‑assisted tools can turn rough ideas into working demos in hours instead of weeks. They help you scaffold codebases, generate UI from prompts or designs, and wire up data so you can validate scope and risk with clients quickly.