Category: Artificial Intelligence(AI)

Introduction: RAG systems live or die by their chunking strategy. Chunk too large and you waste context window space with irrelevant content. Chunk too small and you lose semantic coherence, making it hard for the LLM to understand context. The right chunking strategy depends on your document types, query patterns, and retrieval approach. This guide… Continue reading

Introduction: LLM APIs are inherently slow—even fast models take hundreds of milliseconds per request. When you need to process multiple prompts, make parallel API calls, or handle high-throughput workloads, synchronous code becomes a bottleneck. Async patterns let you overlap I/O wait times, dramatically improving throughput without adding complexity. This guide covers practical async patterns for… Continue reading

Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Hit these limits and your application grinds to a halt with 429 errors. Effective rate limiting isn’t just about staying under limits; it’s about maximizing throughput while maintaining reliability. This guide covers practical rate limiting patterns: token bucket algorithms… Continue reading

Introduction: Off-the-shelf embedding models work well for general text, but domain-specific applications often need better performance. Fine-tuning embeddings on your data can dramatically improve retrieval quality—turning a 70% recall into 90%+ for your specific use case. The key is creating high-quality training data that teaches the model what “similar” means in your domain. This guide… Continue reading

Introduction: Not all queries are equal—some need fast, cheap responses while others require deep reasoning. Query routing intelligently directs requests to the right model, index, or processing pipeline based on query characteristics. Route simple factual questions to smaller models, complex reasoning to GPT-4, and domain-specific queries to specialized indexes. This approach optimizes both cost and… Continue reading

Introduction: LLM applications are notoriously hard to test. Outputs are non-deterministic, quality is subjective, and traditional unit testing doesn’t capture the nuances of language generation. Yet shipping untested LLM features is a recipe for embarrassing failures—hallucinations, off-brand responses, or security vulnerabilities. This guide covers practical testing strategies: deterministic unit tests for prompt templates, evaluation suites… Continue reading

Introduction: Context windows are the lifeblood of LLM applications—they determine how much information your model can process at once. Even with 128K+ token models, you’ll hit limits when dealing with long documents, conversation histories, or multi-document RAG. Poor context management leads to truncated information, lost context, and degraded responses. This guide covers practical strategies for… Continue reading

Introduction: Prompt injection is one of the most significant security risks in LLM applications. Attackers craft inputs that manipulate the model into ignoring its instructions, leaking system prompts, or performing unauthorized actions. As LLMs become more integrated into production systems—handling sensitive data, executing code, or making API calls—the attack surface grows dramatically. This guide covers… Continue reading

Introduction: Evaluating LLM outputs is fundamentally different from traditional ML metrics. You can’t just compute accuracy when there’s no single correct answer, and human evaluation doesn’t scale. This guide covers the full spectrum of LLM evaluation: automated metrics like BLEU, ROUGE, and BERTScore for measuring similarity; semantic metrics that capture meaning beyond surface-level matching; LLM-as-judge… Continue reading

Introduction: Vector databases are the backbone of modern AI applications—powering semantic search, RAG systems, and recommendation engines. But as your vector collection grows from thousands to millions of embeddings, naive approaches break down. Query latency spikes, memory costs explode, and recall accuracy degrades. This guide covers practical optimization strategies: choosing the right index type for… Continue reading