Category: Artificial Intelligence(AI)

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.… Continue reading

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… Continue reading

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,… Continue reading

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… Continue reading

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:… Continue reading

Introduction: Evaluating LLM outputs is fundamentally different from traditional ML evaluation. There’s no single ground truth for creative tasks, quality is subjective, and outputs vary with each generation. Yet rigorous evaluation is essential for production systems—you need to know if your prompts are working, if model changes improve quality, and if your system meets user… Continue reading

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… Continue reading

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… Continue reading

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… Continue reading

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… Continue reading