In the rapidly evolving landscape of Large Language Model (LLM) applications, prompt engineering has emerged as a critical discipline. As teams scale their AI applications, managing prompts across different versions, environments, and use cases becomes increasingly complex. This is where LangSmith's prompt management capabilities shine.
A comprehensive guide to implementing Langfuse features for production-ready AI applications, covering prompt management, tracing, evaluation, and observability.
This guide covers:
This article documents the complete OpenRouter integration implemented in Nomadically.work, using DeepSeek models exclusively through a unified API.
This page documents an AI-first enrichment pipeline that turns a company website into a clean, structured company profile you can safely persist into your database and expose through GraphQL.
This bulk processor extracts structured skill tags for job postings using an AI pipeline that combines:
This article documents an evaluation harness for a Remote EU job classifier—but the real focus is AI observability: how to design traces, spans, metadata, scoring, and run-level grouping so you can debug, compare, and govern LLM behavior over time.
This article documents a production-grade architecture for generating research-grounded therapeutic content. The system prioritizes verifiable artifacts (papers → structured extracts → scored outputs → claim cards) over unstructured text.
You can treat this as a “trust pipeline”: retrieve → normalize → extract → score → repair → persist → generate.
