DevTech Insights

Content Moderation and Safety Filters for LLM Apps: A Practical Guide

Learn how to implement robust content moderation and safety filters for LLM applications to prevent toxic outputs and ensure compliance.

Regression Testing for LLM Outputs: Catching Quality Drift

Learn how to implement robust regression testing for LLM applications to detect quality drift, ensure consistency, and maintain reliability as models and pro...

LLM Routing and Model Selection: Smart Gateways for Cost and Quality

Learn how to implement intelligent LLM routing and model selection strategies to optimize costs and maintain high-quality outputs in production systems.

Building Reliable Tool Calling for LLM Agents

Learn how to build robust, production-ready tool calling for LLM agents. Covers schemas, error handling, retries, and real-world patterns.

Prompt Caching Strategies: Reducing Token Cost and Latency in LLM Applications

Learn how to implement prompt caching strategies to significantly reduce LLM API costs and latency. Practical guide with code examples for production systems.

LLM Observability: Tracing, Evaluations, and Langfuse Deep Dive

Master LLM observability with Langfuse. Learn how to implement tracing, automated evaluations, and production monitoring for reliable AI applications.

Building Multi-Agent Systems with LangGraph and Java

Learn how to build production-ready multi-agent systems in Java using LangGraph. Covers architecture, state management, and practical code examples.

Agentic RAG: Combining Retrieval with Autonomous Agents

Learn how Agentic RAG transforms static retrieval systems into dynamic, autonomous workflows using LLMs, tools, and multi-step reasoning.

Model Context Protocol (MCP): What Backend Devs Need to Know

Explore how Model Context Protocol standardizes AI-tool integration. Learn backend implementation strategies, architecture, and practical code examples for J...

Self-Hosting Open-Source LLMs: A Cost-Benefit Analysis for Engineering Teams

Discover if self-hosting open-source LLMs is worth it. We analyze hardware costs, inference performance, and operational overhead to help you decide.