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The idea of UI generated on the fly instead of pre-built reached production in 2025. After a year of real-world use, the balance is more nuanced than the initial enthusiasm suggested.
Direct Preference Optimization (DPO) and its variants, IPO, KTO, and SimPO, have displaced RLHF as the preferred alignment method for language models: they drop the separate reward model, cut training cost, and are easier to reproduce. RLHF still has an edge only for frontier models with very large budgets.
Synthetic data has moved from a precarious substitute for real data to a central component of modern model training: the most reliable pattern expands a real core of 500 examples with thousands of synthetic paraphrases, provided you validate diversity, correctness, and distribution, and keep at least 30% real data to avoid model collapse.
Hybrid RAG in 2026 combines dense and lexical search fused with RRF, cross-encoder reranking over the top-50 candidates, structure-aware chunking, and continuous evaluation with Ragas or TruLens. It is the pattern that survives in serious production systems three years after the initial embeddings boom.
The first invoice for a production agent usually runs double or triple the estimate. This article walks through five real levers, in priority order, caching, routing, context control, batching, and telemetry, to cut cost without touching perceived quality.
LLM red teaming has gone from an esoteric activity to a mandatory practice. With the OWASP Agentic Top 10 and the CSA Agentic AI Red Teaming Guide converging on shared vocabulary, this is the operational playbook any team deploying agents needs to have.
Prompt engineering has moved from viral tricks to a discipline with reproducible patterns: few-shot, chain-of-thought, and structured output with function calling. Teams treating prompts like code (versioned, tested, and monitored) get consistently better results than those who improvise.
La factura de IA en las empresas ha dejado de ser anecdótica. Entre tokens de modelos frontera, GPUs reservadas que nadie usa y pipelines RAG con cachés mal configuradas, muchos equipos pagan diez veces lo que deberían. Guía de FinOps específico para IA sin relatos promocionales.
Knowledge graphs spent two decades waiting for their moment. With LLMs now bridging free text and formal ontology, and the GraphRAG pattern already mature, the technology is back in the spotlight. Time to look at why it finally fits and where it actually pays off.
Large language models have spent two years promising effortless documentation for code, APIs and architecture. After watching dozens of projects try it, clear patterns emerge for where it works and where it just becomes more debt.
Guardrails frameworks promise to filter language-model inputs and outputs to block data leaks, harmful content, or hallucinations. After evaluating four of the most popular ones in production, I cover what they actually do, what latency and billing cost they add, and when they pay off over simpler controls.
Agents that chain calls to models, tools and memory are hard to debug without instrumentation designed for them. After a long year running agents in production, I cover what to measure first, which standards are consolidating, and which costly mistakes are avoided by getting the traces right from the start.
A caching proxy in front of a language model can cut the token bill significantly, but it introduces subtle risks if the design is not careful. Which cache types work in production, where the usual traps sit, and how to add them without degrading the experience.
Un enrutador de inferencia decide qué modelo atiende cada petición en función de coste, latencia y complejidad. Bien diseñados reducen la factura de tokens sin que el usuario perciba degradación; mal diseñados introducen fallos sutiles difíciles de depurar.
Probar sistemas que incluyen modelos de lenguaje rompe la primera regla del testing: la misma entrada da la misma salida. Analizo las estrategias que han funcionado tras un año largo integrando IA en productos reales, por qué los tests deterministas tradicionales no bastan y cómo plantear un cinturón de pruebas que capture regresiones sin bloquearse en la varianza.
The term Agent OS has spent a year gaining traction across research and product circles. It describes a layer that goes well beyond an agent library: request scheduling, context management, persistent memory, and isolation. A look at the real state of that concept.
Model Context Protocol turns ten months old since Anthropic's announcement, and it is no longer just a proposal: hundreds of servers, cross-vendor implementations and a public registry now back it. A look at what has worked, what is still weak, and why 2025 marks the shift from curiosity to basic infrastructure.
After months of rumors, OpenAI released GPT-5 in early August. The first weeks of real-world use show a picture less spectacular than the marketing suggested and more useful than many expected. It is worth separating what is genuinely new from what is merely incremental.
Small language models have become genuinely useful. Phi-3.5, Gemma 2, and Llama 3.2 fit on modest hardware and solve bounded tasks without reaching the cloud. A look at where they fit on the factory floor and when skipping the large model pays off.
El RAG de 2023 era búsqueda vectorial con un LLM detrás. El de 2025 es un sistema híbrido que combina vectores, búsqueda léxica y grafos de conocimiento. Qué ha cambiado, dónde funciona cada pieza y qué decisiones marcan la diferencia entre un RAG útil y uno decepcionante.
Google publicó Gemini 2.5 Pro en vista previa en marzo y la versión general llegó en junio. El salto respecto a Gemini 2.0 no está solo en puntuaciones sino en dos frentes prácticos: ventana de contexto utilizable en serio y multimodalidad que deja de ser demostración para convertirse en herramienta.
Anthropic presentó Claude Opus 4 y Claude Sonnet 4 el 22 de mayo de 2025, el primer salto grande de nomenclatura desde la serie 3.5. Un mes de uso real en código, documentación técnica y agentes para separar lo que ha mejorado de lo que sigue igual.
For a decade, knowledge graphs were an academic idea with few real use cases, held back by the cost of building and maintaining the schema. LLMs have changed that equation: they now extract entities automatically and help anchor answers, audit reasoning, and support agents without hallucinating.
Un sistema RAG sin evaluación continua se degrada en silencio. Los índices cambian, los modelos se actualizan, los usuarios preguntan cosas nuevas. Este es un repaso práctico de qué métricas vigilar y cómo montar el cuadro de mando que avisa antes del incidente.
