Category: Artificial Intelligence(AI)

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: […]

Introduction: LLM costs can spiral quickly in production systems. A single GPT-4 call might cost pennies, but multiply that by millions of requests and you’re looking at substantial monthly bills. The good news is that most LLM applications have significant optimization opportunities—often 50-80% cost reduction is achievable without sacrificing quality. The key strategies are semantic […]

Introduction: Conversation state management is the foundation of building coherent, context-aware AI assistants. Without proper state management, every message is processed in isolation—the assistant forgets what was discussed moments ago, loses track of user preferences, and fails to maintain the thread of complex multi-turn conversations. Effective state management involves storing conversation history, extracting and persisting […]

Introduction: Document processing is the foundation of any RAG (Retrieval-Augmented Generation) system. Before you can search and retrieve relevant information, you need to extract text from various file formats, split it into meaningful chunks, and generate embeddings for vector search. The quality of your document processing pipeline directly impacts retrieval accuracy and ultimately the quality […]

Introduction: Streaming LLM responses transforms the user experience from waiting for complete responses to seeing text appear in real-time, dramatically improving perceived latency. Instead of staring at a loading spinner for 5-10 seconds, users see the first tokens within milliseconds and can start reading while generation continues. But implementing streaming properly involves more than just […]