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 […]
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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 →Large Language Models Deep Dive: Understanding the Engines Behind Modern AI
Go beyond the basics and understand how LLMs actually work. Master prompting techniques, compare models, and learn cost optimization strategies for production use.
Read more →Event-Driven Architecture on GCP: Mastering Cloud Pub/Sub for Real-Time Systems
Google Cloud Pub/Sub provides the foundation for event-driven architectures at any scale, offering globally distributed messaging with exactly-once delivery semantics and sub-second latency. This comprehensive guide explores Pub/Sub’s enterprise capabilities. Cloud Pub/Sub Architecture Overview Pub/Sub Architecture: Topics, Subscriptions, and Delivery Guarantees Pub/Sub implements a publish-subscribe pattern where publishers send messages to topics and subscribers receive […]
Read more →Generative AI Fundamentals: A Practical Guide to the Technology Reshaping Software
Cut through the hype and understand what Generative AI actually is, how it works, and why it matters. A hands-on introduction for developers and architects ready to build with LLMs.
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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