Category: Emerging Technologies

Introduction: Getting LLMs to produce consistently formatted output is one of the most practical challenges in production AI systems. You need JSON for your API, but the model sometimes wraps it in markdown code blocks. You need a specific schema, but the model invents extra fields or omits required ones. You need clean text, but […]

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 […]

Introduction: As LLM applications grow in complexity, managing prompts becomes a significant engineering challenge. Hard-coded prompts scattered across your codebase make iteration difficult, A/B testing impossible, and debugging a nightmare. Prompt template management solves this by treating prompts as first-class configuration—versioned, validated, and dynamically rendered. A good template system separates prompt logic from application code, […]

Introduction: Debugging LLM chains is fundamentally different from debugging traditional software. When a chain fails, the problem could be in the prompt, the model’s interpretation, the output parsing, or any of the intermediate steps. The non-deterministic nature of LLMs means the same input can produce different outputs, making reproduction difficult. Effective chain debugging requires comprehensive […]

Introduction: Choosing the right embedding model is one of the most impactful decisions in building semantic search and RAG systems. The embedding model determines how well your system understands the semantic meaning of text, how accurately it retrieves relevant documents, and ultimately how useful your AI application is to users. But the landscape is complex: […]