Guides for LLM observability, prompt management & AI evaluation
How-tos, guides, and deep dives on LLM observability, AI monitoring, prompt optimization, LLM evals and AI judges, RAG evaluation, LLM traces, agents, security & governance, and AI engineering.
AI Cost Governance for Teams: Budgets, Attribution & Controls
Set spend caps, attribute cost to teams and features, alert on anomalies, and gate new spend through approvals so the AI bill stays governed, not just tracked.
Read more →Best LLM Gateway Platforms in 2026
How the leading LLM gateway platforms compare for 2026. OpenRouter, LiteLLM, Portkey, Kong, Cloudflare, Vercel, Helicone, and Bedrock, with a comparison table.
Read more →Claude Code Cost in 2026: Plans, Real Prices & How to Cut Them
Claude Code starts at $20/mo on Pro and bills per token on the API. See every plan, hidden usage limits, and the levers that keep agentic spend predictable.
Read more →Claude Code Cost Calculator: Estimate the Bill & 5 Ways to Cut It
Estimate your Claude Code bill with a copy-ready token formula, current Anthropic rates, and five sourced ways to cut it.
Read more →Claude vs GPT vs Gemini: API Pricing Compared (2026)
Compare Anthropic, OpenAI, and Google per-million-token input and output rates, context windows, and prompt caching that change the real bill.
Read more →How to Implement LLM-as-a-Judge: A Practical Guide
Implement LLM-as-a-judge in five steps: write a judge prompt, score single responses or pairs, mitigate bias, and calibrate against human labels.
Read more →LLM Auto-Routing: Cutting Cost Without Cutting Quality
Auto-routing sends each request to the right model at the right price. See how it picks a model, what it costs in complexity, and how to guard output quality.
Read more →LLM Cost Optimization: 9 Tactics (2026 Guide)
Nine tactics to cut LLM spend: model routing, prompt caching, batch, output limits, context control, spend caps, token budgets, and attribution.
Read more →LLM Evaluation: The Complete Guide (2026)
Learn what LLM evaluation covers: quality, safety, cost, and latency, the metric categories that matter, and how to gate regressions in CI/CD.
Read more →LLM Evaluation Metrics Dictionary: Definitions and Use Cases
LLM evaluation metrics: a practical dictionary of BLEU, ROUGE, faithfulness, hallucination, RAG precision and recall, G-Eval, LLM-as-a-judge, cost, latency.
Read more →LLM Gateway vs API Gateway: What's the Difference?
An API gateway routes service traffic. An LLM gateway adds model routing, caching, fallback, and cost guardrails. See when you need each.
Read more →OpenRouter Alternatives: Which Gateway Fits Your Stack?
Compare the best OpenRouter alternatives for multi-provider access, governance, caching, and routing: Portkey, LiteLLM, Vercel, Cloudflare, and more.
Read more →Prompt Caching and Model Routing: Stack the Savings Without Losing Cache
Prompt caching cuts repeated input to about a tenth of the base rate. Model routing picks the right model per request. Run both without breaking the cache.
Read more →The True Cost of Agentic Coding: Claude Code, Codex & Cursor
Claude Code, OpenAI Codex, GitHub Copilot and Cursor all bill on tokens now. Here is what each really costs per month and what moves the bill.
Read more →What is an LLM Gateway? (2026 Guide)
An LLM gateway routes your calls to any provider through one OpenAI-compatible endpoint. Here is how it works, what it solves, and how the pieces fit.
Read more →What is LLM-as-a-Judge? And How It Works
LLM-as-a-judge uses one model to score another model's output. See how judges prompt and score, the bias risks, and when they beat human review.
Read more →Agent Evaluation: Why Passing Evals Isn't Enough
Output-based evals miss how agents actually fail. Measure tool-calling accuracy, task completion, and multi-step reasoning with trace-aware judges.
Read more →Multi-Turn LLM Evaluation: Metrics That Actually Matter
Score the conversation, not the last message - context retention, goal completion, and consistency across the whole thread.
Read more →Human-in-the-Loop Evaluation: When Humans Beat AI Judges
Calibrate AI judges with human judgment - route the hard calls to reviewers and feed annotations back into your dataset.
Read more →LLM Experimentation: Comparing Prompts, Models, and Agents
Turn intuition into evidence with controlled A/B tests across prompts, models, and tools - and pick a winner by the data.
Read more →Building Your LLM Eval Framework and Running It in CI/CD
Start small, adopt a minimal metric set, and gate every pull request - the eval you run beats the one you plan.
Read more →LLM Observability: The Complete Guide to AI Observability
What to track on every LLM call - latency, cost, tokens, quality - and the observability tools that make AI observability actionable.
Read more →LLM Evals & AI Judges: A Practical Guide to AI Evaluation
From LLM boolean and range judges to code and deterministic evaluators - how to build an LLM evaluation pipeline that catches regressions.
Read more →LLM Monitoring & AI Model Monitoring: A Practical Playbook
Latency, error rate, throughput, cost, and quality drift - the AI monitoring metrics that keep production LLM apps healthy.
Read more →LLM Traces: Debugging Multi-Step Agent Workflows
Trace IDs let you follow RAG pipelines and agent loops across many requests. A practical guide to tracing LLM workflows.
Read more →Prompt Management as Code: Git for AI Prompts
Prompt repositories, prompt git workflows, pull requests, and deploy by push - the Git-native way to manage prompts at scale.
Read more →RAG Evaluation: How to Measure Retrieval Quality
Groundedness, faithfulness, context relevance, and citation accuracy - the metrics that tell you if your RAG pipeline actually works.
Read more →Prompt Optimization: Techniques That Actually Work
System messages, variables, few-shot examples, and chain-of-thought - plus how to measure prompt optimization with evals.
Read more →Inside the AI Prompt Playground: Test Before You Ship
Test prompts against live models, tune parameters, and stream responses - how a prompt playground fits into your workflow.
Read more →Security & Governance for LLM Applications
Prompt injection, PII redaction, content moderation, budgets, and audit logs - governing LLM usage across the enterprise.
Read more →AI Engineering: Building Production-Grade LLM Systems
From gateway and prompt management to LLM observability, monitoring, and evals - the full stack for production LLM systems.
Read more →Try LLM observability & prompt management for yourself
Route, evaluate, and monitor every AI request from one OpenAI-compatible platform.