Retrieval-Augmented Generation and AI agents are two important concepts in modern AI application development. They are often mentioned together because both can make Large Language Model applications more capable. However, they solve different problems. RAG helps an AI model retrieve relevant information before generating an answer. AI agents help an AI system decide what actions […]
AI agents are becoming an important part of modern artificial intelligence development. Instead of simply responding to a prompt, an AI agent can understand a goal, decide what actions to take, use tools, access external information, and continue working until it completes a task. Learning the theory behind AI agents is useful, but one of […]
Artificial intelligence is moving beyond systems where a single AI model handles every task on its own. Modern AI applications are increasingly using multiple AI agents that work together, communicate, divide responsibilities, and solve complex problems collaboratively. This approach is known as a Multi-Agent System (MAS). Multi-agent systems can be useful for AI assistants, software […]
AI agents can understand goals, use tools, access memory, retrieve information, and perform multi-step tasks. But when building an agent-based application, an important question appears: Should you use one AI agent or multiple specialized agents? These two approaches are commonly called: and: Both can be useful. The right choice depends on the complexity of your […]
Large Language Models can understand questions, generate text, summarize information, write code, and reason about many types of problems. But an LLM by itself has important limitations. For example, it may not automatically be able to: To perform these tasks, an AI agent needs access to tools. Tools connect an AI model with external systems, […]
AI agents can use tools, call APIs, search documents, perform calculations, and complete multi-step tasks. But many useful agents need something else: Memory. Without memory, an AI agent may forget information as soon as the current interaction ends. For example, imagine you tell an agent: Later, you ask: If the agent has memory, it may […]
AI chatbots are useful because they can understand questions and generate responses. But what if you want an AI system that can do more than simply talk? For example, imagine an AI assistant that can: This type of system is commonly called an AI agent. Instead of only generating text, an AI agent can decide: […]
When building an AI application, one of the most common questions is: Should I use RAG or fine-tuning? Both techniques can improve an AI system, but they solve very different problems. RAG is mainly used when you want an AI model to answer questions using external or up-to-date knowledge. Fine-tuning is mainly used when you […]
Many applications need to perform tasks automatically at specific times or intervals. Sending daily reports, cleaning old records, synchronizing data, generating backups, sending reminders, and updating external APIs are common examples. Running these tasks manually is inefficient and can easily lead to missed jobs. Node.js job scheduling allows developers to execute tasks automatically based on […]
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