As artificial intelligence continues to transform how we work and learn, it’s crucial to understand the core building blocks of AI-powered systems. Whether you’re exploring AI agents, building automation workflows, or learning to craft better prompts, this guide distils practical insights from five essential publications.
To use AI effectively, it’s important to understand the difference between LLMs, automation, and AI agents. This video AI Agents, Clearly Explained breaks it down simply: LLMs respond to prompts, automation follows fixed logic, but AI agents’ reason, act, and iterate on their own. Knowing the distinction helps avoid overhyping simple tools and unlocks the real value of autonomous systems.
1. Gemini for Workspace Prompt Guide (Google)
This guide explains how to get better results from Google’s Gemini AI in tools like Gmail, Docs and Sheets. The key is learning how to write clear and focused prompts so the AI knows exactly what you want it to do. Whether you’re replying to emails, summarising documents or working with spreadsheets, the way you ask matters.
To get the most out of Gemini, be specific about your intent. For example, say “summarise this” or “translate into plain English” rather than just pasting in content. It also helps to mention who the message is for and the tone you want, like professional, friendly or casual, so Gemini can match the style. Including links, background info or past content gives the AI helpful context.
You don’t need to get it perfect on the first try. You can start with a rough prompt and then ask Gemini to rewrite or improve it. Examples include asking Gmail to draft a polite reply with next steps, using Docs to expand bullet points into a full summary, or asking Sheets to explain trends in your data in plain language.
This kind of prompting is useful for things like internal communication, report writing, job ads, quizzes and more. The more clearly you guide Gemini, the more useful and accurate the results will be.
2. A Practical Guide to Building Agents (OpenAI)
This guide explains how to build smart AI systems, called agents, that can plan, take action and make decisions on their own. Unlike simple automation tools, these agents don’t need constant human supervision. They can figure out what to do, use tools like search engines or messaging platforms, and adjust their actions as they go.
An agent works by combining three things: a powerful language model to handle thinking and planning, tools to get or send information, and clear instructions that guide what it’s allowed to do. The tools it uses can be for finding data, making updates, or coordinating tasks.
There are different ways to structure these agents. A simple setup might have one agent completing a task by itself. A more complex system could involve multiple agents working together, with one acting as a manager that delegates jobs.
To keep things safe and reliable, these systems need checks in place. That includes filtering results, scrubbing personal data, and setting rules for when a human should step in. Real-world examples include handling customer refund requests, reviewing vendor options or processing messy form data automatically.
3. Prompt Engineering Whitepaper (Google)
This whitepaper explains how to write better prompts to get more accurate and useful responses from language models like ChatGPT or Gemini. It doesn’t just cover what to say, but also how to fine-tune the AI’s behaviour using settings like temperature and structure.
There are a few main prompting techniques. You can ask a direct question (zero shot), or give one or two examples first to guide the answer (few shot). You can also ask the AI to explain its reasoning step by step using a method called Chain of Thought, which is especially helpful for maths or logic tasks. Another method, called ReAct, helps the AI think, then use tools like search or calculators before responding. You can also give detailed instructions about tone and format, such as “summarise this in 5 dot points.”
You can adjust how the model responds using output settings. For example, temperature controls how random the answer is. Lower values make it more predictable, higher values make it more creative. Top K and Top P settings control which words the model is allowed to choose from. Stop sequences tell it when to stop writing to avoid going off track.
Good prompt design is like training a new staff member. Be clear, give structure, and don’t be afraid to experiment with different approaches. Even small tweaks can improve how the AI performs.
4. Identifying and Scaling AI Use Cases (OpenAI)
This playbook helps teams discover and evaluate useful AI projects using a simple, structured approach. It introduces the idea of “AI use case types”, common tasks where AI tools like language models tend to be especially effective.
Examples include creating content like summaries or emails, helping with research by scanning documents, supporting developers with bug fixes or code suggestions, and analysing data to spot patterns. It can also help with brainstorming ideas, drafting plans, or automating routine tasks like sending updates.
To uncover good opportunities, the guide suggests tools like “anti to do lists”, things you wish AI could do, and prompt templates based on specific roles. It also recommends running workshops to map out team workflows and find time consuming bottlenecks.
To stay focused, it uses a simple scoring system: compare the impact of an idea to how much effort it takes to build. This helps teams focus on easy wins with high value, and recheck priorities every few months as tools and skills improve.
5. Building Effective AI Agents (Anthropic)
This document is a practical guide for building and improving AI agent systems. It focuses on reusable design patterns and helps you decide when to use a full agent setup instead of a simpler automated workflow.
The key design approach is to keep things simple. Don’t use an agent if a basic structured workflow will do the job. Instead of relying heavily on agent libraries, use clear logic, task chaining and feedback loops.
Some common patterns include chaining tasks step by step, routing prompts to the right logic path, and handling steps in parallel. You can also use an evaluator and optimiser model setup, where one AI checks the work and another improves it. Another useful setup is the orchestrator and worker pattern, where one agent manages others.
For tools, it’s best to use mistake-proofing methods, avoid hard-to-generate formats and structure your prompts to give the AI enough time to think.
Agents are most useful for complex tasks with multiple steps, when human review doesn’t scale well, or when you need the AI to keep improving its output through feedback.
Final thoughts
AI can feel overwhelming, but understanding the core building blocks like prompting, automation and agents makes it much easier to spot where it can add real value. These five resources offer a practical foundation for anyone looking to use AI more effectively, whether you’re writing prompts, mapping out workflows or building systems that can act on their own.
The key takeaway is this: clear prompts unlock useful responses, automation handles routine tasks, and AI agents take things further by reasoning, acting and improving without constant human input. Knowing the difference helps you avoid the hype and focus on solutions that actually work.
If your business is curious about where AI fits or how to get started, Watsy can help. We work with clients to turn ideas into working AI powered tools, from simple automations to fully functional agents. Whether you’re testing a concept or ready to scale, we will help you design, build and launch with confidence.