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 […]
Read more →Category: Artificial Intelligence(AI)
Embedding Space Analysis: Visualizing and Understanding Vector Representations
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 […]
Read more →Context Compression Techniques: Fitting More Information into Limited Token Budgets
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. […]
Read more →LLM Output Formatting: Getting Structured Data from Language Models
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 […]
Read more →Retrieval Augmented Fine-Tuning (RAFT): Training LLMs to Excel at RAG Tasks
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 […]
Read more →Prompt Templates and Management: Building Maintainable LLM Applications
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, […]
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