#evaluation
24 articles
How AI coding assistants work
The engineering behind Copilot, Cursor, and Claude Code: repo maps, tree-sitter indexing, edit formats, long-file strategies, and how to evaluate code generation properly.
Long Context vs RAG: The Million-Token Question
When does stuffing a million tokens beat retrieval? The empirics on lost-in-the-middle accuracy, context-stuffing costs, and when RAG still wins at 1M tokens.
Reading LLM Benchmarks Without Getting Fooled
LLM leaderboard scores are routinely inflated by contamination and benchmark saturation. Learn how to read them skeptically — and what to do instead.
Red-Teaming LLMs: From Manual Probing to Automated Adversarial Testing
How to run a structured red-team engagement against your LLM system — scoping, tooling (Garak, PyRIT, PromptFoo), automation, and what the results actually mean.
Tracing and observability tooling: LangSmith, Langfuse, Braintrust, and Arize compared
A production-honest comparison of LangSmith, Langfuse, Braintrust, and Arize Phoenix for LLM tracing, evaluation, and quality monitoring in 2026.
RAG evaluation, failure modes, and when to use long context or fine-tuning instead
Measure a RAG pipeline with RAGAS, spot the failure modes that kill production quality, and decide between RAG, long context, or fine-tuning.
Prompt Versioning, Evaluation, and LLMOps: Treating Prompts Like Code
How production teams version, test, and regression-test prompts the same way they ship software — CI/CD gates, eval datasets, and the tools that make it tractable.
Hallucination mitigation and output validation for production systems
Factuality vs. faithfulness, grounding techniques, structured outputs, LLM-as-judge, and conformal prediction — practical controls that reduce hallucination in deployed LLMs.
Agent Evaluation: Trajectories, Tool Calls, and Multi-Step Correctness
End-to-end task success is not enough for agents. Learn to evaluate at three levels: outcome, trajectory soundness, and per-component correctness — with code.
Agent Evaluation and Why Agents Fail in Production
Why 85% of agent pilots never reach production scale — compounding error rates, LLM-judge bias, and the six evaluation layers that catch what offline tests miss.
A/B Testing Prompts and Models in Production
How to run statistically valid experiments on LLM prompt variants and model swaps — shadow mode, canary rollout, LLM-as-judge scoring, and when to call the winner.
RAG Evaluation with Ragas: Measuring Retrieval and Generation Separately
Measure retrieval and generation separately with RAGAS — faithfulness, context precision, answer relevancy — and catch the failures end-to-end scores hide.
Synthetic Data Engineering: Pipelines, Quality, and Model Collapse Risks
How to build production-grade synthetic data pipelines for LLM fine-tuning—Magpie, Evol-Instruct, quality filters—and why model collapse is a real but avoidable failure.
LLM Observability: Monitoring Quality, Cost, and Drift in Production
Traditional APM tells you the HTTP call succeeded. LLM observability tells you whether the answer was any good — covering traces, quality scores, cost anomalies, and drift.
Golden Datasets: Building the Ground Truth Your Evals Depend On
A concrete recipe for constructing and maintaining LLM evaluation datasets — sourcing, sizing, versioning, and wiring them into CI so regressions are caught before they ship.
Prompt Versioning and CI/CD for LLM Changes
How to treat prompts as versioned, tested artifacts and build a deployment pipeline that gates every change on automated evaluation before it reaches users.
LLM-as-Judge: Calibrating Your Automated Evaluator
How to build an LLM judge that actually agrees with your domain experts — covering bias failure modes, rubric design, calibration targets, and production cost math.
Offline vs Online Evaluation: Building Your Two-Layer Safety Net
Pre-deployment regression suites catch what you break; production sampling catches what happens to you. Here is how to build both layers and wire them together.
Why Eval Is Hard: The Measurement Problem in LLM Applications
Standard software metrics miss quality regressions in LLM apps. Learn why subjectivity, Goodhart traps, and silent failures demand a dedicated eval discipline.
The Model Landscape: Choosing Without Getting Played
A decision framework for picking between closed frontier models, open-weight, and reasoning tiers — plus benchmark literacy so leaderboard scores stop misleading you.
RAG in One Pass: Build, Measure, Improve
Build a complete RAG pipeline end-to-end — ingest, chunk, embed, index, retrieve, generate — with evaluation wired in from day one so you catch failures before users do.
Eval First: Building the Safety Net Before You Need It
Why LLM eval is hard, how 50 real examples beat 5,000 synthetic ones, and how to wire offline CI gates and production sampling into a two-layer safety net.
Fine-Tuning: The Decision Framework
When to fine-tune, when to stay with RAG or prompting, and how LoRA, DPO, and distillation actually work — with the cost math to make the call.
LLMOps: Shipping and Operating AI Products
The operational layer that separates lasting AI products from perpetual pilots: gateways, eval pipelines, prompt CI/CD, observability, and the data flywheel.