Do AI models actually have memory? No. On their own they store nothing, so ask a follow-up and the model forgets what you said unless it's inside the context. So how does an agent stay coherent across multiple steps? You feed the whole conversation back in. Every single turn,...
Do AI models actually have memory? No. On their own they store nothing, so ask a follow-up and the model forgets what you said unless it's inside the context. So how does an agent stay coherent across multiple steps?
You feed the whole conversation back in. Every single turn, the entire history gets sent into the context window again. The model isn't remembering; it's rereading everything that happened, every time.
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How do AI agents actually use tools like web search? The model can't magically go online, it can only predict text. So when it wants to search, it outputs a structured message that looks like code, saying it wants to use the search tool. The code running the model (the agent...
How do AI agents actually use tools like web search? The model can't magically go online, it can only predict text. So when it wants to search, it outputs a structured message that looks like code, saying it wants to use the search tool.
The code running the model (the agent harness) sees that, runs the tool, gets the result, and hands it back to the model to process. That handoff (model decides, harness executes) is the whole way AI agents work at scale.
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How do AI agents get long-term memory or access to your documents? A vector database. The short version: you convert your data into embeddings (numerical representations of meaning), store them, then give the agent a search tool so it pulls only the relevant information for...
How do AI agents get long-term memory or access to your documents? A vector database. The short version: you convert your data into embeddings (numerical representations of meaning), store them, then give the agent a search tool so it pulls only the relevant information for whatever you're asking.
This pattern is called RAG (retrieval augmented generation), and it's how agents answer questions about documents and remember long-term facts even when the context window is full of the current conversation.
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Behind the popular AI tools lies a crucial bit of tech called a transformer. It's what the "T" stands for in ChatGPT, just FYI. Ania tells you all about it here.
Behind the popular AI tools lies a crucial bit of tech called a transformer. It's what the "T" stands for in ChatGPT, just FYI. Ania tells you all about it here.
What's the difference between a chatbot and an AI agent? It's the most common confusion in AI right now, and it comes down to two abilities. A chatbot only talks: message in, text out. An agent adds tool calling (taking real actions like searching the web or updating a...
What's the difference between a chatbot and an AI agent? It's the most common confusion in AI right now, and it comes down to two abilities. A chatbot only talks: message in, text out.
An agent adds tool calling (taking real actions like searching the web or updating a database) and the ability to loop step by step instead of stopping after one reply. Bottom line: a chatbot answers a question, an agent is given a goal and goes and accomplishes it.
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AI is fast, but it often solves the wrong problem. If a junior dev asks AI to fix slow checkout code, they might get a script. But if the real bottleneck is a messy database design, that code won't fix a thing. The software engineers who will thrive in the AI age aren't the...
AI is fast, but it often solves the wrong problem. If a junior dev asks AI to fix slow checkout code, they might get a script. But if the real bottleneck is a messy database design, that code won't fix a thing.
The software engineers who will thrive in the AI age aren't the ones who just write code—it’s the ones who understand the complete system. ⚙️
Ready to level up? Our IT Fundamentals Course teaches you system-level thinking, the full software development life cycle, and how to spot the real problems.
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