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Latest from AgentGuide

31 recent articles

AgentGuide

Apr 6, 2026 · Artificial Intelligence

How to Optimize RAG System Performance: From Evaluation Metrics to Tuning Strategies

The article explains how to improve Retrieval‑Augmented Generation (RAG) systems by interpreting three key metrics—context recall, context precision, and answer correctness—and provides concrete step‑by‑step actions such as checking the knowledge base, upgrading embedding models, rewriting queries, adding a rerank model, and refining prompts and generation parameters.

RAGcontext precisioncontext recall

0 likes · 7 min read

How to Optimize RAG System Performance: From Evaluation Metrics to Tuning Strategies

AgentGuide

Apr 3, 2026 · Artificial Intelligence

How to Evaluate RAG Systems: Key Metrics and the Ragas Framework

The article explains how to assess Retrieval-Augmented Generation (RAG) projects using the Ragas automated evaluation framework, detailing four key dimensions—recall quality, answer faithfulness, answer relevance, and context utilization—and describes the underlying metrics for both retrieval and generation stages.

LLMMetricsRAG

0 likes · 5 min read

How to Evaluate RAG Systems: Key Metrics and the Ragas Framework

AgentGuide

Apr 2, 2026 · Artificial Intelligence

Understanding ReAct: The Reason‑Act Loop Behind LLM Agents

The article explains ReAct—a Reason‑Act framework for large language model agents that observes, reasons, takes actions via tools, receives feedback, and iterates—highlighting its distinction from plain QA, its step‑by‑step workflow, practical importance, and a weather‑query example.

AI WorkflowLLM agentsReAct

0 likes · 5 min read

Understanding ReAct: The Reason‑Act Loop Behind LLM Agents

AgentGuide

Mar 30, 2026 · Artificial Intelligence

What Is a Multi-Agent System? Three Core Working Modes Interviewers Expect

The article explains that multi-agent systems typically operate in three patterns—sequential execution, parallel execution, and an evaluator-optimizer loop—covers when each pattern is appropriate, and offers interview tips on how to discuss these designs effectively.

AI interviewEvaluator-OptimizerParallel Execution

0 likes · 3 min read

What Is a Multi-Agent System? Three Core Working Modes Interviewers Expect

AgentGuide

Mar 27, 2026 · Artificial Intelligence

What Are Skills in LLM Agents? How They Work and When to Use Them

The article defines Skills as structured local folders that encapsulate domain‑specific processes, knowledge, and tools for large language models, contrasts them with temporary Prompts, outlines suitable use cases, details their components, and explains their on‑demand loading mechanism that saves tokens.

Agent developmentOn-demand LoadingSkills

0 likes · 4 min read

What Are Skills in LLM Agents? How They Work and When to Use Them

AgentGuide

Mar 25, 2026 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and Why Must Large Models Look Up Information First?

Retrieval‑Augmented Generation (RAG) lets large language models first fetch relevant documents and then generate answers, addressing the inability of models to answer private or domain‑specific queries by precisely feeding them the most pertinent knowledge.

EmbeddingRAGVector Database

0 likes · 5 min read

What Is Retrieval‑Augmented Generation (RAG) and Why Must Large Models Look Up Information First?

AgentGuide

Mar 24, 2026 · Artificial Intelligence

What I Learned Moving from Backend Engineering to AI Agent Development

The author, a former backend engineer turned AI Agent developer, explains how LLM uncertainty, context engineering, shifting code responsibilities, workflow standards, new failure modes, and the ReAct paradigm shape modern Agent development, and outlines tasks best suited—or unsuited—for LLMs.

AI AgentContext EngineeringLLM

0 likes · 6 min read

What I Learned Moving from Backend Engineering to AI Agent Development

AgentGuide

Mar 22, 2026 · Artificial Intelligence

How to Design Prompt Engineering in Your Project: A Complete Workflow

The article outlines a systematic Prompt Engineering process that starts with defining task goals and metrics, structures prompts into modular components, uses offline evaluation and bad‑case analysis, incorporates RAG or tools when needed, and continuously monitors accuracy, hallucination, latency and cost.

AI WorkflowFew-shotRAG

0 likes · 7 min read

How to Design Prompt Engineering in Your Project: A Complete Workflow

AgentGuide

Mar 21, 2026 · Artificial Intelligence

What Is the Model Context Protocol (MCP)? An Interview‑Style Deep Dive

The article explains MCP (Model Context Protocol) as a standardized interface for AI applications to access external data sources and tools, compares it with function calling, outlines its security considerations, architecture, resource types, and transport options such as Stdio and Streamable HTTP.

AI applicationsClient-Server ArchitectureJSON-RPC

0 likes · 5 min read

What Is the Model Context Protocol (MCP)? An Interview‑Style Deep Dive

AgentGuide

Mar 19, 2026 · Artificial Intelligence

What Exactly Is an AI Agent? Complete Interview Guide

This article breaks down the concept of AI agents for interview preparation, covering their definition, core components like planning, memory, and tool use, differences from plain LLM chats, real‑world challenges, typical use cases, detailed component analysis, and a runnable pseudo‑code example.

AI AgentLLMmemory

0 likes · 9 min read

What Exactly Is an AI Agent? Complete Interview Guide