Tag: #llmops

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• Self-Hosting Langfuse — Deployment Paths, Secrets, and What Catches You on First Boot

  Running Langfuse yourself means standing up two containers and four datastores at once. Working from the official documentation, this post lays out the docker compose path and the Helm chart path, which secrets you have

  2026-08-14 · 9 min read #observability#langfuse#self-hosting#docker-compose#kubernetes

• Why Langfuse Puts Traces in ClickHouse — How the Storage Layer Splits the Work

  The first surprise when you self-host Langfuse is that there is not one datastore but four. ClickHouse, Postgres, Redis, and object storage each take a different share. Working from the official documentation, this post

  2026-08-14 · 10 min read #observability#langfuse#clickhouse#architecture#postgres

• The Langfuse Tracing Data Model — How Trace, Observation, and Score Hold One Execution

  Looking at the Langfuse UI first tells you nothing about what you are looking at. This post starts from the shape of the data Langfuse collects. What a trace groups, what separates a span from a generation from an event,

  2026-08-14 · 8 min read #observability#langfuse#llm-tracing#data-modeling#opentelemetry

• After You Have Collected It — Langfuse Dashboards, the Metrics API, and Scores on Traces

  Collecting traces and getting answers out of them are different jobs. This post lays out which metric axes Langfuse offers and which dimensions you have to slice them by, then walks the Metrics API v2 query structure as

  2026-08-14 · 9 min read #observability#langfuse#llm-evaluation#metrics-api#dashboard

• What LLM Ops Actually Does — Reproducibility, Contamination, Checkpoints, Promotion, and Rollback

  This post organizes LLM Ops as a list of responsibilities, not a list of tools. It covers what belongs in a run manifest that lets you reconstruct a training run, how to prevent and audit eval-set contamination, the form

  2026-08-02 · 14 min read #mlops#llmops#reproducibility#evaluation#model-registry

• The Complete LLMOps Guide: Three-Axis Versioning of Model, Prompt, and Eval Set, Canary, Cost Control, and the Platform Team (2025)

  Building an LLM product fast has become easy. Running it sustainably is the hard part. Three-axis versioning across model, prompt, and eval set; Shadow/Canary/Blue-Green deployment; cost control through tokens, caching,

  2026-04-15 · 11 min read #llmops#mlops#devops#canary#cost-control

• Prompt Caching for Agent Apps: A Practical Guide to Lower Cost and Latency

  A practical guide to prompt caching for agent applications, including why it matters, how OpenAI and Anthropic differ, prompt structuring patterns, ROI scenarios, common mistakes, and a migration checklist.

  2026-04-12 · 7 min read #prompt-caching#latency#cost-optimization#ai-agent#llmops

• MLOps & Model Lifecycle Management: MLflow, DVC, and LLMOps Complete Guide

  A comprehensive guide to ML production pipelines covering MLOps maturity models, MLflow experiment tracking, DVC data versioning, feature stores, and LLMOps.

  2026-03-17 · 16 min read #mlops#mlflow#dvc#llmops#featurestore

• Gemini API in Production: Prompting, Guardrails, Evaluation, and Cost Control

  A production guide to building on the Gemini API with practical prompting, tool and schema design, safety handling, evaluation loops, and cost-aware operating patterns.

  2026-03-17 · 4 min read #gemini#generative-ai#ai#llmops#prompt-engineering

• LLMOps Platform Architecture Guide: Model Deployment, Monitoring, and A/B Testing

  A comprehensive guide to designing and implementing an LLMOps platform. Covers vLLM/TGI-based model serving, token usage/latency/quality monitoring, prompt version management, A/B testing framework, guardrail integration

  2026-03-13 · 14 min read #ai-platform#llmops#model-serving#monitoring#ab-testing

• Chatbot Tool Calling Guardrails Design: Safe Tool Invocation Architecture in Practice

  Covers guardrail design for safely operating tool-calling chatbots in production. Addresses schema validation, permission control, policy engines, risk scoring, observability, and regression testing with code.

  2026-03-04 · 18 min read #chatbot#tool-calling#guardrails#ai-agent#security

• RAG Chatbot Evaluation in Practice: From Offline/Online Quality Measurement to Production Guardrails

  Covers the evaluation system for reliably operating RAG chatbots in production. Addresses offline benchmarks, LLM-as-a-Judge, online experiments, alert thresholds, and regression prevention pipelines with code.

  2026-03-04 · 16 min read #chatbot#rag#evaluation#llmops#production