The first observation of 'What course articles do you have?' was contaminated by an old cache entry. A real cache miss retrieved all four university maps but spent 51.169 seconds across three Writer and Critic passes; after catalog-specific retrieval and review fixes, one uncached production observation passed q21 in 26.821 seconds.
Ask AI keeps golden contracts, offline fixtures, live SSE output, and production observations separate. A passing fixture proves harness reproducibility; public sources can measure expected-source recall, but they do not expose hidden ranked chunks or establish model-graded faithfulness.
Benchmark scores in model releases have three common traps: cherry-picking (only showing wins), contamination (test data leaking into training), and saturation (when everyone scores 90%+, the benchmark stops being useful). The most manipulation-resistant signal is Chatbot Arena's Elo ranking — real humans, blind voting, uncontrolled questions.
Small models don't fail at reasoning first — they fail at format stability: tool-call JSON, diff hunk arithmetic, and context budgets all break. The mature harnesses build evals on real model behavior (pi's model-backed evals, OMP calibrating benchmarks from real session logs, Codex even relaxing its parser for weaker models). looplane picks the narrowest but hardest path: one fixture, five real Ollama runs, a manifest declaring exactly which files and patch fragments count as success — and M2's failure kept verbatim as evidence. Never pass mock off as E2E; never spin partial success into full passes.
None of the five reference projects enforces 'all declared verification commands pass' at the harness level: pi leaves verification to the model, OpenCode and Codex put it in the system prompt, Claude Code uses a separate adversarial verifier subagent but as a soft contract, and only OMP's cleanse actually runs checks from harness code. looplane takes the hardest path: if files changed, every declared verification command must pass before terminal_reason=verified; with no changes, checks don't rerun (no_changes). Whether to verify is decided by code, not by the model.
2023 was the first full academic year after ChatGPT, and LLMs rewrote the NLP conference agenda. ACL's Best Papers examined humor understanding and the propagation of political bias; an EMNLP Best Paper explained in-context learning through information flow; and the HackAPrompt competition paper also won an EMNLP Best Paper award, signaling that security research had entered the mainstream. The year's largest shift was from asking how to make models more accurate to asking how we can tell when a model is misleading us.
You cannot take model vendors' self-reported scores at face value. This guide covers the most important independent evaluation platforms, domain benchmarks, adoption indicators, and official sources in 2026: what each measures, how to read it, where it is biased, and which figures matter for different use cases.
Evaluation is Agent Platform's quality immune system: instead of collecting statistics only after a run, it enforces checks throughout Pre-run, In-run, and Post-run execution. Seven eval categories cover Flow → Step → Skill → Artifact → Evidence → Policy → Regression. A Skill release must pass five gates—Trigger, Functional, Policy, Regression, and Human Review—and any failure blocks it. The Learning Loop moves from Run signals through Proposal, Human Review, Sandbox Eval, Quality Gate, and Publish, under one strict rule: agents propose, humans review, and eval gates decide whether a change can ship.
A Skill is a versioned, installable, and auditable capability package. Its dual-file architecture separates metadata from instructions, explicit binding replaces model-driven routing, and every invocation is recorded. The Learning Loop turns run signals into proposals, sandbox evaluations, human review, and publication while enforcing the principle: agents propose, humans review, and evals serve as the gate.
Phoenix is an MIT-licensed open-source LLM observability and evaluation platform. It collects traces with OpenTelemetry and OpenInference, turns production failures into versioned datasets, compares prompt, model, or RAG changes in experiments, then writes code, human, and LLM evaluator scores back as annotations. It is not Arize AX, and self-hosting defaults require security work.
Lecture 13 separates alignment into specification, distribution shift, oversight, and corrigibility, then uses benchmark selection, leakage, and post-hoc selection experiments to show why a final paper cannot audit an autonomous research workflow.
Lecture 9 begins with log-log linear relationships between data and error, then uses scaling laws to compare architectures, optimizers, batches, and model-data allocations. The Chinchilla dispute shows how fitting methods, observed ranges, and deployment objectives change the answer.
DSPy replaces handwritten prompt strings with task Signatures, execution Modules, and Optimizers that compile better instructions and examples against a dataset and metric.
LangSmith structures LLM applications as projects, traces, runs, and threads, then uses datasets, evaluators, and experiments to turn production failures into offline regression tests. It observes any LLM application and does not require LangChain.
The repository has a 20-query Traditional Chinese/English golden dataset, but no document-level qrels, retrieval runs, raw latency data, or executable benchmark script. Reporting Recall@k, MRR, or nDCG as measured results would therefore be dishonest; this article defines the contract needed to run them reproducibly.
Units 08–12 turn a base model into an interactive system: pre-training establishes capability, post-training shapes behavior, and generation plus evaluation determine how outputs are used.
Units 13–14 connect models to external knowledge; A3 requires data collection, QA annotation, indexing, and ablations under CPU and latency constraints.
Promptfoo plugins generate risk probes, strategies transform attacks, targets execute the system, and graders judge outcomes; useful red teams exercise the full agent application rather than only a foundation model.
CS224U's teaching material isn't a slide deck — it's an Apache-2.0 GitHub repo holding the lecture notebooks, all three assignments, and the grading document for the final project. But the on-campus course has skipped three straight academic years since Spring 2023, and ExploreCourses has it back on the books for Spring 2026-27. The official description still lists relation extraction and semantic parsing; the 2023 syllabus covers neither. And the data-loading cell in the first assignment breaks in a fresh environment today, on a Hugging Face compatibility change.
CS329A is built around the generation–verification gap: models can produce the right answer but can't tell which one it is. The conclusion the course draws about itself matters more — today's methods make models more consistent, not smarter. Nine lectures are public, out of twenty.
Four certifications genuinely test RAG and retrieval evaluation: AWS AIF-C01 (chapters 2 and 3 total 52%, covering RAG, vector stores, and FM evaluation metrics), AWS AIP-C01 (11 of the 27 skill points in its 31% Domain 1 sit in vector storage and RAG), NVIDIA NCP-AAI (Knowledge Integration 10% plus Evaluation and Tuning 13%), and Microsoft AI-500 ('multi-agent RAG architecture' inside its 30–35% Develop area). Google PMLE contributes exactly one LLM-as-a-judge objective, and Claude CCDV-F — the developer certification people most readily assume covers RAG — has no retrieval objective across its eight domains, with Eval at just 2.6%. Includes a same-vendor foundational-vs-professional comparison, a four-vendor terminology map, non-transferable objectives, and a practice project.
A BCG experiment found a jagged frontier: inside it, AI substantially improved consultants' work; outside it, AI made results worse — and people fell asleep at the wheel. The lecture also takes a strong position: avoid fine-tuning wherever possible, because by the time you're done tuning, the next model already beats your fine-tuned version.
Andrew Ng demonstrates error analysis on a deep researcher: columns are the pipeline stages, rows are 10 to 100 queries, you only look at the ones that went badly, and you mark each cell where something broke. The percentages don't have to sum to 100%. He says it takes three or four hours and saves weeks of going the wrong direction — and the fraction of people who actually do it is far below 100%.
Ask a model what a goose looks like to it and it draws a whole flock — because the labeled data tagged a flock as 'goose,' so it thinks the flock is the label. This lecture gives seven ways to open a CNN up, then says honestly: applied to transformers, even the frontier of this research only explains two layers.
Salesforce's number from 20,000 deployments: 90% of the work on an agent happens after launch, the reverse of traditional software. Stripe merges 1,300 PRs a week with no human-written code, and credits the environment rather than the model.
Even with temperature=0, LLM outputs can still fluctuate by up to 15% in practice. To rigorously compare agent changes, you need a frozen golden set, at least 3 runs per query averaged out, LLM-as-judge blind evaluation (pairwise preference flip rate reaches 35%), and paired statistical tests -- not just running each version once and going by feel.
Langfuse is currently the most mature open-source LLM Observability platform. This post covers four core capabilities — Tracing, Prompt Management, Evaluation, and Datasets — showing you how to use them in real projects.
RAG has evolved far beyond simple 'search + generate' into a technology ecosystem spanning ten generations — and since 2025 into an Agentic/Reasoning era. This article is a systematic navigation guide: from Naive RAG to Multi-Agent/LongRAG across ten generations, the post-ten Agentic Era (Search-R1/RL search, MCP, GraphRAG 3.x, vision-native retrieval), retrieval strategies, chunking, embedding, reranking, evaluation frameworks, observability, and cost optimization. Each topic has a dedicated deep-dive article.
No industry standard mandates one RAG evaluation tool. Measure retrieval, generation, and operations separately, then choose Promptfoo, RAGAS, DeepEval, or TruLens for the actual stack.