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Offline Evaluation of RAG-Grounded Answers in LaunchDarkly AI Configs

Jun 22nd 2026, 15:00 by Scarlett Attensil

This tutorial shows you how to run an offline LLM evaluation on the RAG-grounded support agent you built in the Agent Graphs tutorial, using LaunchDarkly AI Configs, the Datasets feature, and built-in LLM-as-a-judge scoring. You’ll build a RAG-grounded test dataset, run it through the Playground with a cross-family judge, and learn how to read each failing row as a dataset issue, an agent issue, or judge calibration noise.

Here’s how it works. The LaunchDarkly Playground evaluates a single model call against a prompt and dataset you configure. By pre-computing your RAG retrieval offline and baking the chunks directly into each dataset row, you turn that call into a high-value generation test: the model in the Playground receives the same documentation context it would in production, so the eval measures how well your agent reasons over real grounded input.

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Devs Don't Want More Dashboards; They Want Self-Healing Systems

Jun 22nd 2026, 14:00 by Thomas Johnson

Every observability vendor's roadmap right now includes some version of "AI-powered insights." Smarter dashboards, with an assistant bolted on, to help you make sense of the data faster.

That's not what developers are asking for.

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Fix the Target, Precompute Once: A Backend-Free Word-Ladder Solver With a BFS Distance Field

Jun 22nd 2026, 13:00 by horus he

When you build an interactive puzzle, the latency budget is unforgiving. Every keystroke needs an answer that feels instant. A daily word-ladder game has to do three of those instant jobs at once: confirm that the word a player typed is legal, tell them the best possible score for the day, and, on request, reveal the shortest solution. I ran into all three while building Poople, a daily game where you change a 4-letter word into POOP one letter at a time, and the fix turned out to be a tidy lesson in trading repeated computation for one-time precomputation.

The obvious approach is to run a graph search whenever you need an answer. That works, and it is also the wrong default here. This article walks through why, then shows how fixing the destination word lets you replace every future search with a single offline pass plus an O(1) lookup. The whole solver then runs in the browser, with no backend and no per-request search.

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Generative Engine Optimization: How to Make Your Content Visible to AI

Jun 22nd 2026, 12:00 by Sibanjan Das

There was a time when SEO meant stuffing keywords into meta tags to be noticed by Google's crawler. That changed over time, and the approach was refined with structured data, backlinks, page authority, and semantic search.

Now the rules are changing again. People are no longer just typing queries into a search engine and browsing the blue links. They ask ChatGPT, Perplexity, Claude, or Gemini, and they get a direct answer. If an AI answers the question, your carefully optimized page is invisible, even if it ranks #1 on Google.

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