Introduction: AI agents become powerful when they can use tools—searching the web, querying databases, calling APIs, executing code. But tool selection is where many agent implementations fail. The agent might choose the wrong tool, call tools with incorrect parameters, or get stuck in loops trying tools that won’t work. This guide covers practical patterns for […]
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LLM Fine-tuning Fundamentals: When, Why, and How to Customize Language Models
Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model for your specific use case. While prompt engineering works for many applications, fine-tuning offers advantages when you need consistent formatting, domain-specific knowledge, or reduced latency from shorter prompts. This guide covers practical fine-tuning: when to fine-tune versus prompt engineer, preparing training data, running fine-tuning jobs […]
Read more →Prompt Versioning and Management: Bringing Software Engineering Rigor to LLM Development
Introduction: Prompts are code. They determine how your LLM application behaves, and like code, they need version control, testing, and deployment pipelines. Yet many teams treat prompts as afterthoughts—hardcoded strings scattered across the codebase, changed ad-hoc without tracking. This leads to regressions, inconsistent behavior, and difficulty understanding why outputs changed. This guide covers practical prompt […]
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 […]
Read more →LLM Output Parsing: Extracting Structured Data from Language Model Responses
Introduction: LLMs generate text, but applications need structured data. Parsing LLM outputs reliably is one of the most common challenges in production systems. The model might return JSON with extra text, miss required fields, use unexpected formats, or hallucinate invalid values. This guide covers practical parsing strategies: using structured output modes, building robust parsers with […]
Read more →Advanced Retrieval Strategies for RAG: From Query Transformation to Multi-Stage Pipelines
Introduction: Retrieval is the foundation of RAG systems. Poor retrieval means irrelevant context, which leads to hallucinations and wrong answers regardless of how capable your LLM is. Yet many RAG implementations use naive approaches—single-stage vector search with default settings. This guide covers advanced retrieval strategies: query transformation techniques, hybrid search combining dense and sparse methods, […]
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