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 →Category: Artificial Intelligence(AI)
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 […]
Read more →LLM Model Selection: Choosing the Right Model for Every Task
Introduction: Choosing the right LLM for your task is one of the most impactful decisions you’ll make. Use a model that’s too small and you’ll get poor quality. Use one that’s too large and you’ll burn through budget while waiting for slow responses. The landscape changes constantly—new models launch monthly, pricing shifts, and capabilities evolve. […]
Read more →Structured Generation Techniques: Getting Reliable JSON from LLMs
Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
Introduction: LLM applications are notoriously difficult to debug. Unlike traditional software where errors are obvious, LLM issues manifest as subtle quality degradation, unexpected costs, or slow responses. Proper observability is essential for production LLM systems. This guide covers monitoring strategies: tracking latency, tokens, and costs; implementing distributed tracing for complex chains; structured logging for debugging; […]
Read more →LLM Security Best Practices: Protecting AI Applications from Attacks
Introduction: LLM applications face unique security challenges. Prompt injection attacks can hijack model behavior, sensitive data can leak through responses, and malicious outputs can harm users. Traditional security measures don’t fully address these risks—you need LLM-specific defenses. This guide covers practical security strategies: validating and sanitizing inputs, detecting prompt injection attempts, filtering sensitive information from […]
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