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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.
You start a new AI feature in .NET, search for "Semantic Kernel", and copy the first sample you find. Six months later the SDK only gets security fixes, and every new capability you need lives somewhere else.
Semantic Kernel and AutoGen are in maintenance mode. Microsoft Agent Framework is their successor, built by the same teams.
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 (GPT, Claude, Gemini, Llama, Mistral, Grok, DeepSeek, Qwen, 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.
Everyone wants to integrate AI into their application and workflows, but often times you're using the wrong tool for the task. Most AI work in your app isn't writing, it's deciding.
System 1 models are built for that, you ask typed questions and you get typed answers with probabilities in blazing fast times. Generative System 2 models like Claude and ChatGPT shine at jobs that need reasoning or words. Don't guess which one you need - test them on your own data and let cost, accuracy and speed decide.
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.
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.
“Your loan is approved under Section 42 of the Banking Act 2025.” One problem: there is no Section 42.
That single hallucination triggered a regulator investigation and a six-figure penalty. In high-stakes domains like finance, healthcare, legal and compliance zero-error tolerance is the rule. Your assistant must always ground its answers in real, verifiable evidence.
AI is a powerful tool, however, sometimes it simply makes things up, aka hallucinates. AI hallucinations can sometimes be humorous, but it is very bad for business!
AI hallucinations are inevitable, but with the right techniques, you can minimize their occurrence and impact. Learn how SSW tackles this challenge using proven methods like clean data tagging, multi-step prompting, and validation workflows.
Want to supercharge your business with Dataverse AI integration? This guide pulls together proven strategies and practical recommendations to help your organization maximize the latest Copilot, Copilot Studio agents, and Model Context Protocol (MCP) innovations for Dataverse.
Anthropic launched the Model Context Protocol (MCP) in November 2024 to streamline AI integration, quickly earning adoption from major players like OpenAI and Microsoft. This standard has revolutionized how businesses connect AI assistants to their applications, creating seamless, context-aware experiences without complex technical implementations.
For non-technical business owners and decision-makers, MCP offers a straightforward way to future-proof applications and tap into the growing AI ecosystem.
Connecting an LLM-driven agent to multiple external services might look simple in a diagram, but it's often a nightmare in practice.
Each service requires a custom integration, from decoding API docs, handling auth, setting permissions, to mapping strange data formats. And when you build it all directly into your agent or app, it becomes a brittle, tangled mess that's impossible to reuse.
You ship a new AI feature and expect costs and latency to drop thanks to prompt caching, but nothing changes. After investigation, you discover that a timestamp in the system prompt or a small tool schema change invalidated the cache on every request. Small structural mistakes can completely eliminate caching benefits.
You finish a Claude Code session at 5pm on Tuesday. It knows your coding preferences, the test command, and the auth-module quirk you corrected twice.
Wednesday morning, you start a new session. It tries the wrong test command again.
AI Agents have become a daily driver for most developers, but very few people stop and reflect on how they are actually using them. Bad habits compound silently. Soon you're reaching for the same broken prompt every morning, re-explaining the same three project gotchas to every fresh session, and losing an hour debugging because the agent claimed to be "done" without testing anything.
/insights is Claude Code's built-in reflection tool. It reads the last 30 days of your conversation history and turns it into a personalized report on how you actually work with your agent, where you get stuck, and what to you can change.