AI has six main categories, each focusing on specific tasks and techniques. Imagine AI as a school called 'AI will always love you' with different classes, each dedicated to a unique skill. These categories are: 1 - Machine Learning 2 - Natural Language Processing 3 - Expert Systems 4 - Computer Vision 5 - Speech Recognition 6 - Robotics Machine Learning (ML) is a part of AI that helps computers learn from data, find patterns, and improve over time without needing humans to tell them what to do. By looking at lots of existing information, ML algorithms can spot trends, make choices, and improve their skills based on new examples. It is like the math class where students learn to solve problems by practicing with examples. Real world application: Recommendations on what to watch on Netflix. Natural Language Processing (NLP) is a part of AI that helps computers understand, interpret, and create human language. It lets machines process and analyze text or speech like people do, making it easier for them to work with language. NLP is the language class, where students learn how to read, write, and talk. Real world application: Autocorrect and autocomplete text. An Expert System is a computer program that mimics how a human expert makes decisions in a specific area. It uses set rules to analyze information and draw conclusions, applying knowledge from that field to help solve problems and provide advice. It is similar to a science class where students apply specific rules and knowledge from their field to solve problems. Real world application: Diagnosis of bacterial infections. Computer Vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world, such as images and videos, allowing them to perform tasks like object recognition, image classification, and scene understanding. It is like the art class that helps students learn to interpret images and recognize patterns in visuals. Real world application: Facial recognition technology.

You've seen that user prompts can be unpredictable and are always goal-driven. So how do you work with that variability? By understanding what makes a user prompt effective. The best practices below will help you design clearer prompts so the AI agent can focus on reaching its goal. 🎯 Be specific about the task Tell the AI agent exactly what your desired outcome is for the task. 🔎 Example: "Search our CRM for all customers who purchased in Q4 and generate a 150-word retention strategy for each." #️⃣ Put your request at the beginning of the prompt and include separators When your prompt includes both instructions and background information, use separators like ### to make the distinction clear. This helps the AI agent understand the task and what output you want before reading any background information. 🔎 Example:
"Identify the main themes in this customer feedback:
###
'The product is great but shipping took forever.'
###" ℹ️ Provide relevant context for the task Give background that helps the agent prioritize what matters for the specific request. 🔎 Example: "Generate a weekly performance report for the sales team showing lead conversion rates and top performers." 🔑 Use leading words Start your prompt with specific keywords or phrases that signal what type of output you want. This guides the AI in the right direction immediately. 🔎 Example: "Evaluate vendor proposals..." or "List the top 5 cities to live in." ✍🏻 Show examples of desired output Providing examples of the desired result for the task can significantly improve the AI agent's output. 🔎 Example: "Summarize customer feedback like this: [example of customer feedback report]." ✨ Iterate and refine If the first response misses the mark, add more details to guide the agent closer to what you want. 🔎 Example: "This is helpful, but I need more specific numbers. Add actual metrics and data points from the report I shared earlier." You don't need to apply all of these best practices for every user prompt. However, the more complex your task, the more these practices matter. Use them when you need precise outputs, when handling multi-step tasks, or when initial responses miss the mark. Think of them as ways to get better results faster, especially for challenging prompts.

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