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… Continue reading

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:… Continue reading

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… Continue reading

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… Continue reading

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… Continue reading

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… Continue reading

Introduction: Query understanding is the critical first step in building intelligent AI systems that respond appropriately to user requests. Before your system can retrieve relevant documents, call the right tools, or generate helpful responses, it needs to understand what the user actually wants. This involves intent classification (is this a question, command, or conversation?), entity… Continue reading

Introduction: Hybrid search combines the best of both worlds: the semantic understanding of vector search with the precision of keyword matching. Pure vector search excels at finding conceptually similar content but can miss exact matches; pure keyword search finds exact terms but misses semantic relationships. Hybrid search fuses these approaches, using vector similarity for semantic… Continue reading

Introduction: LLM APIs fail. Rate limits hit, services go down, models return errors, and responses sometimes don’t meet quality thresholds. Building reliable AI applications requires robust fallback strategies that gracefully handle these failures without degrading user experience. A well-designed fallback system tries alternative models, implements retry logic with exponential backoff, caches successful responses, and provides… Continue reading