Introduction: Embeddings are the foundation of modern AI applications—they transform text, images, and other data into dense vectors that capture semantic meaning. Understanding how embedding models work, their strengths and limitations, and how to choose between them is essential for building effective search, RAG, and similarity systems. This guide covers the landscape of embedding models: […]
Read more →Category: Technology Engineering
Technology Engineering
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 →Guardrails and Safety Filters: Protecting LLM Applications from Harmful Content
Introduction: LLMs can generate harmful, biased, or inappropriate content. They can be manipulated through prompt injection, jailbreaks, and adversarial inputs. Production applications need guardrails—safety mechanisms that validate inputs, moderate content, and filter outputs before they reach users. This guide covers practical guardrail implementations: input validation to catch malicious prompts, content moderation using classifiers and LLM-based […]
Read more →Testing LLM Applications: Unit Tests, Integration Tests, and Evaluation
Introduction: Testing LLM applications presents unique challenges compared to traditional software. Outputs are non-deterministic, quality is subjective, and the same input can produce different but equally valid responses. This guide covers practical testing strategies: unit testing with mocked LLM responses, integration testing with real API calls, evaluation frameworks for quality assessment, and regression testing to […]
Read more →LlamaIndex: The Data Framework for Building Production RAG Applications
Introduction: LlamaIndex (formerly GPT Index) is the leading data framework for building LLM applications over your private data. While LangChain focuses on chains and agents, LlamaIndex specializes in data ingestion, indexing, and retrieval—the core components of Retrieval Augmented Generation (RAG). With over 160 data connectors through LlamaHub, sophisticated indexing strategies, and production-ready query engines, LlamaIndex […]
Read more →Function Calling Deep Dive: Building LLM-Powered Tools and Agents
Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just describing what to do, the model can actually do it—query databases, call APIs, execute code, and interact with external systems. OpenAI’s function calling (now called “tools”) and similar features from Anthropic and others let you define available functions, and the model […]
Read more →