Introduction: Production LLM applications require comprehensive logging and tracing to debug issues, monitor performance, and understand user interactions. Unlike traditional applications, LLM systems have unique logging needs: capturing prompts and responses, tracking token usage, measuring latency across chains, and correlating requests through multi-step workflows. This guide covers practical logging patterns: structured request/response logging, distributed tracing […]
Read more →Guardrails and Safety for LLMs: Building Secure AI Applications with Input Validation and Output Filtering
Introduction: Production LLM applications need guardrails to ensure safe, appropriate outputs. Without proper safeguards, models can generate harmful content, leak sensitive information, or produce responses that violate business policies. Guardrails provide defense-in-depth: input validation catches problematic requests before they reach the model, output filtering ensures responses meet safety standards, and content moderation prevents harmful generations. […]
Read more →Vector Search Optimization: Embedding Models, Hybrid Search, and Reranking Strategies
Introduction: Vector search is the foundation of modern RAG systems, but naive implementations often deliver poor results. Optimizing vector search requires understanding embedding models, index types, query strategies, and reranking techniques. The difference between a basic similarity search and a well-tuned retrieval pipeline can be dramatic—both in relevance and latency. This guide covers practical vector […]
Read more →LLM Chain Composition: Building Complex AI Workflows with Sequential, Parallel, and Conditional Patterns
Introduction: Complex LLM applications rarely consist of a single prompt—they chain multiple steps together, each building on the previous output. Chain composition enables sophisticated workflows: retrieval-augmented generation, multi-step reasoning, iterative refinement, and conditional branching. Understanding how to compose chains effectively is essential for building production LLM systems. This guide covers practical chain patterns: sequential chains, […]
Read more →Async LLM Patterns: Concurrent Execution, Rate Limiting, and Task Queues for High-Throughput AI Applications
Introduction: LLM API calls are inherently I/O-bound—waiting for network responses dominates execution time. Async programming transforms this bottleneck into an opportunity for massive parallelism. Instead of waiting sequentially for each response, async patterns enable concurrent execution of hundreds of requests while efficiently managing resources. This guide covers practical async patterns for LLM applications: concurrent request […]
Read more →Document Processing with LLMs: Parsing, Chunking, and Extraction for Enterprise Applications
Introduction: Processing documents with LLMs unlocks powerful capabilities: extracting structured data from unstructured text, summarizing lengthy reports, answering questions about document content, and transforming documents between formats. However, effective document processing requires more than just sending text to an LLM—it demands careful parsing, intelligent chunking, and strategic prompting. This guide covers practical document processing patterns: […]
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