version: "1.0.0" name: benchmark-qed-autoe description: > Evaluate RAG system outputs using benchmark-qed scoring methods. Use when: running pairwise comparisons, reference-based scoring, assertion-based evaluation (flat or hierarchical), retrieval metrics, or statistical significance tests on RAG outputs. Also use when the user wants to score, compare, or evaluate RAG methods, measure retrieval quality, or run significance tests on benchmark results — even if they don't say "autoe" explicitly.

Benchmark-QED Evaluation (autoe)

Evaluate and compare RAG system outputs using LLM-judged scoring, assertion-based evaluation, and retrieval metrics — all with built-in statistical significance testing.

Prerequisites

• Generated questions/assertions from the autoq pipeline (or your own)
• RAG method answer files (JSON, one per method per question set)
• A valid settings.yaml for the evaluation type
• A configured workspace with valid settings.yaml (use the benchmark-qed-setup skill to initialize and configure)
• LLM API key configured

Run all commands with:

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed <command>

Evaluation Methods Overview

Commands

1. Pairwise Scores

Compare RAG methods using LLM-judged pairwise comparisons.

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe pairwise-scores <config.yaml> <output_dir> [OPTIONS]

Options:

Config requires: base (reference method), others (methods to compare), question_sets, criteria, trials (must be even), llm_config, prompt_config

Default criteria: comprehensiveness, diversity, empowerment, relevance

Output: {question_set}_{base}--{other}.csv, win_rates.csv, winrates_sig_tests.csv

2. Reference Scores

Score generated answers against reference (gold-standard) answers.

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe reference-scores <config.yaml> <output_dir> [OPTIONS]

Config requires: reference, generated (list), criteria, score_min/score_max, trials, llm_config

Default criteria: correctness, completeness. Default score range: 1–10.

Output: reference_scores-{name}.csv, model_usage.json

3. Assertion Scores

Evaluate RAG methods using assertion-based scoring. Auto-detects single-RAG vs multi-RAG config.

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe assertion-scores <config.yaml> <output_dir> [OPTIONS]

Options:

Auto-detection: If the YAML contains a rag_methods key, it runs in multi-RAG mode with automated significance testing. Otherwise, single-RAG mode.

Single-RAG output: assertion_scores.csv, assertion_summary_by_question.csv, eval_summary.json

Multi-RAG output: Per-method scores + significance tests in structured output_dir/

4. Hierarchical Assertion Scores

Score hierarchical assertions (global assertions with supporting local assertions).

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe hierarchical-assertion-scores <config.yaml> <output_dir> [OPTIONS]

Modes: staged (default — evaluate local first, then global) or joint (evaluate together)

Extra field: detect_discovery: true enables detection of novel findings not covered by assertions.

Also auto-detects single vs multi-RAG config (same as assertion-scores).

5. Assertion Significance

Run statistical significance tests on existing assertion scores (no LLM calls).

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe assertion-significance <config.yaml>

Config requires: output_dir, rag_methods, question_sets, alpha, correction_method

Correction methods: holm (default, recommended), bonferroni, fdr_bh

6. Hierarchical Assertion Significance

Significance tests on hierarchical assertion scores.

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe hierarchical-assertion-significance <config.yaml>

Config requires: scores_dir, rag_methods, scores_filename_template, alpha, correction_method, output_dir

7. Generate Retrieval Reference

Generate cluster relevance reference data for retrieval evaluation (one-off prep step).

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe generate-retrieval-reference <config.yaml>

Config requires: llm_config, embedding_config, question source (questions_path or question_sets), text_units_path

Key settings: num_clusters, assessor_type (rationale or bing), semantic_neighbors, centroid_neighbors

8. Retrieval Scores

Evaluate retrieval precision, recall, and fidelity for RAG methods.

uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe retrieval-scores <config.yaml>

Config requires: rag_methods, question_sets, reference_dir, text_units_path, output_dir

Fidelity metrics: js (Jensen-Shannon divergence) or tvd (total variation distance)

Workflow

Quick Evaluation (Assertion-Based)

• [ ] Step 1: Verify questions and answers exist — list the workspace and confirm a settings.yaml (or config.yaml), question JSON files (typically under output/), and your RAG method answer JSONs are present.
• [ ] Step 2: Initialize eval config — use the benchmark-qed-setup skill to create and configure an assertion evaluation workspace.
• [ ] Step 3: Configure settings.yaml with answer paths and assertion paths
• [ ] Step 4: Run evaluation — uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe assertion-scores ./eval_workspace/settings.yaml ./eval_output
• [ ] Step 5: Summarize results — read the CSVs in <output_dir> (e.g. assertion_scores.csv, assertion_summary_by_question.csv) and eval_summary.json directly.

Multi-RAG Comparison

For comparing multiple RAG methods, use multi-RAG config format (include rag_methods key in YAML). This gives you automated pairwise significance testing.

Gotchas

• Config auto-detection: assertion-scores and hierarchical-assertion-scores detect single vs multi-RAG based on the rag_methods key in YAML. Ensure your config matches your intent.
• Trials must be even: For pairwise scores, trials must be even (for counterbalancing). Use 4 as default.
• Stale outputs: Several commands skip existing output files. Use a fresh output directory or delete specific files to force re-evaluation.
• Output is in files: All scores are written to CSV/JSON files. Parse output files, not CLI stdout.
• Long-running: Evaluation with many questions and trials can take hours. Use background execution.
• No `config init` for hierarchical/retrieval: The benchmark-qed-setup skill only supports autoe_assertion, autoe_pairwise, and autoe_reference. For hierarchical, multi-RAG, and retrieval configs, create YAML manually.
• Advanced config types: Use the benchmark-qed-setup skill for configuration guidance on advanced config types.

Azure Blob Storage

All autoe commands support reading config files from Azure Blob Storage using blob:// URIs:

# Config file in blob storage
uvx --from "git+https://github.com/microsoft/benchmark-qed" benchmark-qed autoe assertion-scores \
  blob://my-container/eval/settings.yaml ./eval_output \
  --account-url https://myaccount.blob.core.windows.net

In addition, settings.yaml supports input_storage and output_storage blocks so the evaluation pipeline can read answers/assertions from and write results to Azure Blob Storage:

# Read answers and assertions from blob storage
input_storage:
  type: blob
  container_name: my-datasets
  connection_string: ${AZURE_STORAGE_CONNECTION_STRING}
  # Or use managed identity:
  # account_url: https://myaccount.blob.core.windows.net
 
# Write evaluation output to blob storage
output_storage:
  type: blob
  container_name: my-output
  connection_string: ${AZURE_STORAGE_CONNECTION_STRING}

When using storage blocks, answer_base_path and assertions_path in the config are resolved relative to the storage container (not the local filesystem).

See references/config-reference.md for full StorageConfig fields.