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Traditional keyword search has limitations, but vector databases overcome them by retrieving results based on meaning rather than exact matches. Here’s a closer look at the retrieval pipeline in agentic AI using vector databases:. Embeddings - Raw text gets converted into

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Vector databases are the backbone of agentic AI systems. Why? Because the quality of your embeddings and the effectiveness of your vector database determine whether your AI retrieves meaningful information or fails silently. Traditional keyword search was never enough. Imagine a

When building with LLMs, one of the most important decisions you’ll face is whether to use a proprietary API (like GPT-4, Claude, or Gemini) or an open-weight model (like LLaMA, Mistral, or Qwen). Both paths have strengths , but the right choice depends on what you’re optimizing

RT @MongoDB: Agents 🤝 Memory. We’re excited to announce the MongoDB Store for LangGraph!. In partnership with @LangChainAI, we're making it….

You don’t need an agentic framework to start building with LLMs,especially if your goal is just a quick feature or a lightweight script. Frameworks like LangChain, LangGraph, and CrewAI can be powerful for orchestration and multi-step reasoning, but at the core, they all sit on

ReadyTensor is welcoming community members to contribute articles, tutorials, and technical explainers in support of the growing Agentic AI Developer Certification. At ReadyTensor, we’re passionate about empowering the global AI/ML community by providing a platform to:

One of the best ways to sharpen your skills and gain real-world experience in AI/ML is by contributing to open-source projects. Open source projects are those whose source code is publicly available for anyone to view, use, modify, and contribute to. Many of the tools we rely on

In traditional software systems, failures are usually loud, a crash, an exception, or a 500 error. These signals make it clear something is broken. But in agentic systems, failures are often quiet. The system doesn’t stop running , it just starts behaving in unexpected or

Monitoring vs. Observability in AI Systems.Monitoring is about tracking system metrics from the outside. It watches signals like latency, error rates, and resource usage, and alerts you when they cross a threshold. Observability, on the other hand, lets you look inside the system

Even if you follow the best design patterns, your system can still collapse if you fall into these traps. 1.) One Agent to Rule Them All.That “super-agent” you built to do everything? Yeah, it’ll crumble under its own weight. Specialized roles beat a jack-of-all-trades every

Multi-agent systems shine when solving complex problems that demand specialization. But without a clear design, they can quickly become slow, expensive, unreliable, and hard to debug. A simple mental model helps you design systematically by thinking in three levels of

RT @Hesamation: This guy deserves more attention. He made a super-practical playlist on AI Engineering. Each video covers one major applic….

When building agentic AI systems, you need confidence they’ll perform reliably in the real world and that’s where pytest comes in. Why pytest?. Minimal boilerplate, just use assert. Fixtures to set up reusable test conditions. Parametrization to run the same.

The best developers build with testing in mind, and this applies to agentic systems just as much as any other software. Your systems won’t always live safely inside a notebook. In agentic systems, testing goes far beyond checking the LLM’s accuracy. You’re testing the entire

LangSmith traces every node, logs every LLM and tool call, captures inputs and outputs, and shows exactly how your system’s state evolved ,making debugging AI apps a whole lot easier. Imagine this: you ask your banking chatbot how to report wrongly dispensed cash… and it

LangGraph is especially useful when orchestrating complex multi-agent workflows. Its key concepts;nodes, edges, graphs, and super-steps make it easier to design and coordinate intricate processes. Langraph is built on four core building blocks. State -The current context of

⚠️ Common Pitfalls When Building AI Systems (and How to Avoid Them). When developing AI systems, three of the most common and costly challenges are:.1.)   Hallucination :  When an LLM generates responses that are factually incorrect but sound coherent and convincing. How to

Prompts Are the Blueprint for Your AI’s Behavior.They dictate the model’s behavior, guide its reasoning process, and determine the accuracy, clarity, and usefulness of its responses. In production systems, prompt quality directly impacts reliability, scalability, and user

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