How to Qualify and Evaluate Prompt Changes in GenAI Image Classification SystemsA Systematic Approach to Testing, Measuring, and Validating Vision-Language Model Prompts
Learn how to systematically evaluate and qualify prompt changes in LLM-driven image classification systems using metrics, testing frameworks, and best practices.
Best prompt evaluation tools in 2025: a practical comparison for AI teamsPromptFoo, Braintrust, Langsmith, and Evals compared on the criteria that actually matter in production
Compare the best prompt evaluation tools in 2025 - features, scoring methods, CI integration, and pricing for AI teams building at scale.
The complete prompt evaluation checklist: coverage, scoring, and regression - all in one placeEvery dimension, metric, and failure mode to assess before shipping a prompt to production
A complete prompt evaluation checklist covering test coverage, scoring rubrics, edge case detection, and regression testing for AI systems.
Architecting Production LLM Systems: How AI Gateways, Orchestration, RAG, and Observability Fit TogetherA practical reference architecture connecting OpenRouter, LangChain/LlamaIndex, vector search, structured outputs, and Langfuse into one coherent AI engineering stack
How AI gateways, LangChain/LlamaIndex orchestration, vector search, structured outputs, and Langfuse observability combine into a production LLM architecture.
The Real Skillset of an AI Engineer: Complementary Skills That Actually MatterWhy systems thinking, software engineering, and product sense beat pure model expertise
AI engineering requires far more than prompt writing or model tuning. Learn the complementary skills AI engineers need, from system design and APIs to observability, security, and cost control.
Zero-Shot, Few-Shot, and Tool-Using Agents: Choosing the Right Prompting StrategyHow to decide between instruction-only, examples, and structured tool calls as complexity grows.
Understand zero-shot vs few-shot prompting in modern AI engineering, when each works best, and how tool use and structured outputs change your prompt strategy.
LLM Integrations in Practice: Architecture Patterns, Pitfalls, and Anti-PatternsHow to integrate large language models into real systems without creating fragile, expensive messes
Integrating LLMs into production systems is an engineering problem, not a demo exercise. This post covers proven integration patterns, common mistakes, and what not to build with LLMs.
RAG 101 for AI Engineers: From Naive Retrieval to Production-Grade PipelinesChunking, embeddings, reranking, citations, evaluation, and failure modes explained simply.
A step-by-step guide to building a reliable RAG system, covering chunking, embeddings, retrieval, reranking, context windows, and evaluation tactics for better answers.
Architecting a Scalable Prompt Library: From Abstraction to ImplementationBuilding the Core Layer Between AI Models and Your Application Logic
A prompt library isn't just a collection of strings-it's an architectural layer that defines how your app interacts with AI models. Learn how to design a scalable, testable, and versioned prompt library using proven software engineering patterns, schema validation, and modular composition.