Category: Emerging Technologies

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

Introduction: Retrieval Augmented Generation (RAG) has become the standard pattern for grounding LLM responses in factual, up-to-date information. But basic RAG—retrieve chunks, stuff into prompt, generate—often falls short in production. Queries get misunderstood, irrelevant chunks pollute context, and answers lack coherence. This guide covers advanced RAG patterns that address these challenges: query transformation to improve… Continue reading

Introduction: High-dimensional embeddings from models like OpenAI’s text-embedding-3-large (3072 dimensions) or Cohere’s embed-v3 (1024 dimensions) deliver excellent semantic understanding but come with costs: more storage, slower similarity computations, and higher memory usage. For many applications, you can reduce dimensions significantly while preserving most of the semantic information. This guide covers practical dimensionality reduction techniques: PCA… Continue reading

Introduction: LLM latency is the silent killer of user experience. Even the most accurate model becomes frustrating when users wait seconds for each response. The challenge is that LLM inference is inherently slow—autoregressive generation means each token depends on all previous tokens. This guide covers practical techniques for reducing perceived and actual latency: streaming responses… Continue reading