Category: Emerging Technologies

Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative… Continue reading

Introduction: Prompt debugging is an essential skill for building reliable LLM applications. When prompts fail—producing incorrect outputs, hallucinations, or inconsistent results—systematic debugging techniques help identify and fix the root cause. Unlike traditional software debugging where you can step through code, prompt debugging requires understanding how language models interpret instructions and where they commonly fail. This… Continue reading

Introduction: Batch inference optimization is critical for cost-effective LLM deployment at scale. Processing requests individually wastes GPU resources—the model loads weights once but processes only a single sequence. Batching multiple requests together amortizes this overhead, dramatically improving throughput and reducing per-request costs. This guide covers the techniques that make batch inference efficient: dynamic batching strategies,… Continue reading

Introduction: LLM monitoring is essential for maintaining reliable, cost-effective AI applications in production. Unlike traditional software where errors are obvious, LLM failures can be subtle—degraded output quality, increased hallucinations, or slowly rising costs that go unnoticed until the monthly bill arrives. Effective monitoring tracks latency, token usage, error rates, output quality, and cost metrics in… Continue reading

Introduction: Understanding embedding spaces is crucial for building effective semantic search, RAG systems, and recommendation engines. Embeddings map text, images, or other data into high-dimensional vector spaces where similar items cluster together. But how do you know if your embeddings are working well? How do you debug retrieval failures or understand why certain queries return… Continue reading

Introduction: Context window limits are one of the most frustrating constraints when building LLM applications. You have a 100-page document but only 8K tokens of context. You want to include conversation history but it’s eating into your prompt budget. Context compression techniques solve this by reducing the token count while preserving the information that matters.… Continue reading

Introduction: Getting LLMs to produce consistently formatted output is one of the most practical challenges in production AI systems. You need JSON for your API, but the model sometimes wraps it in markdown code blocks. You need a specific schema, but the model invents extra fields or omits required ones. You need clean text, but… Continue reading

Introduction: Retrieval Augmented Fine-Tuning (RAFT) represents a powerful approach to improving LLM performance on domain-specific tasks by combining the benefits of fine-tuning with retrieval-augmented generation. Traditional RAG systems retrieve relevant documents at inference time and include them in the prompt, but the base model wasn’t trained to effectively use retrieved context. RAFT addresses this by… Continue reading

Introduction: As LLM applications grow in complexity, managing prompts becomes a significant engineering challenge. Hard-coded prompts scattered across your codebase make iteration difficult, A/B testing impossible, and debugging a nightmare. Prompt template management solves this by treating prompts as first-class configuration—versioned, validated, and dynamically rendered. A good template system separates prompt logic from application code,… Continue reading