Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative […]
Read more →Category: Artificial Intelligence(AI)
Retrieval Augmented Fine-Tuning (RAFT): Training LLMs to Excel at RAG Tasks
Introduction: Retrieval Augmented Fine-Tuning (RAFT) represents a powerful approach to improving LLM performance on domain-specific tasks by combining the benefits of fine-tuning with retrieval-augmented generation. Traditional RAG systems retrieve relevant documents at inference time and include them in the prompt, but the base model wasn’t trained to effectively use retrieved context. RAFT addresses this by […]
Read more →Memory Systems for LLMs: Buffers, Summaries, and Vector Storage
Introduction: LLMs have no inherent memory—each request starts fresh. Building effective memory systems enables conversations that span sessions, personalization based on user history, and agents that learn from past interactions. Memory architectures range from simple conversation buffers to sophisticated vector-based long-term storage with semantic retrieval. This guide covers practical memory patterns: conversation buffers, sliding windows, […]
Read more →Building Multi-Agent Workflows: Advanced LangGraph Patterns
Building multi-agent workflows requires careful orchestration. After building 18+ multi-agent systems with LangGraph, I’ve learned what works. Here’s the complete guide to advanced LangGraph patterns for multi-agent workflows. Figure 1: Multi-Agent Architecture with LangGraph Why Multi-Agent Workflows Multi-agent systems offer significant advantages: Specialization: Each agent handles specific tasks Parallelism: Agents can work simultaneously Scalability: Add […]
Read more →LLM Prompt Templates: Building Maintainable Prompt Systems
Introduction: Hardcoded prompts are a maintenance nightmare. When prompts are scattered across your codebase as string literals, updating them requires code changes, testing, and deployment. Prompt templates solve this by separating prompt logic from application code. This guide covers building a robust prompt template system: variable substitution, conditional sections, template inheritance, version control, and A/B […]
Read more →Multi-turn Conversation Design: Building Natural Dialogue Systems with LLMs
Introduction: Multi-turn conversations are where LLM applications become truly useful. Users don’t just ask single questions—they refine, follow up, reference previous context, and expect the assistant to remember what was discussed. Building effective multi-turn systems requires careful attention to context management, history compression, turn-taking logic, and graceful handling of topic changes. This guide covers practical […]
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