FEATURE: Add docs for AI evals (#82)

Author: romanrizzi

docs/06-general-guides/04-ai-evals.md

@@ -0,0 +1,131 @@
+---
+title: Run Discourse AI evals
+short_title: AI evals
+id: ai-evals
+---
+
+# Overview
+
+The Discourse AI plugin ships a Ruby CLI under `plugins/discourse-ai/evals` that exercises AI features against YAML definitions and records results. Use it to benchmark prompts, compare model outputs, and regression-test AI behaviors without touching the app database.
+
+## Core concepts (what users need to know)
+
+- **Eval case**: A YAML definition under `evals/cases/<group>/<id>.yml` that pairs inputs (`args`) with an expected outcome. Evals can check exact strings, regexes, or expected tool calls.
+- **Feature**: The Discourse AI behavior under test, identified as `module:feature_name` (for example, `summarization:topic_summaries`). `--list-features` shows the valid keys.
+- **Persona**: The system prompt wrapped around the LLM call. Runs default to the built-in prompt unless you pass `--persona-keys` to load alternate prompts from `evals/personas/*.yml`. Add multiple keys to compare prompts in one run.
+- **Judge**: A rubric embedded in some evals that requires a second LLM to grade outputs. Think of it as an automated reviewer: it reads the model output and scores it against the criteria. If an eval defines `judge`, you must supply or accept the default judge model (`--judge`, default `gpt-4o`). Without a judge, outputs are matched directly against the expected value.
+- **Comparison modes**: `--compare personas` (one model, many personas) or `--compare llms` (one persona, many models). The judge picks a winner and reports ratings; non-comparison runs just report pass/fail.
+- **Datasets**: Instead of YAML cases, pass `--dataset path.csv --feature module:feature` to build cases from CSV rows (`content` and `expected_output` columns required).
+- **Logs**: Every run writes plain text and structured traces to `plugins/discourse-ai/evals/log/` with timestamps and persona keys. Use them to inspect failures, skipped models, and judge decisions.
+
+## Prerequisites
+
+- Have a working Discourse development environment with the Discourse AI plugin present. The runner loads `config/environment` (defaulting to the repository root or `DISCOURSE_PATH` if set).
+- LLMs are defined in `plugins/discourse-ai/config/eval-llms.yml`; copy it to `eval-llms.local.yml` to override entries locally. Each entry expects an `api_key_env` (or inline `api_key`), so export the matching environment variables before running, for example:
+  - `OPENAI_API_KEY=...`
+  - `ANTHROPIC_API_KEY=...`
+  - `GEMINI_API_KEY=...`
+- From the repository root, change into `plugins/discourse-ai/evals` and run `./run --help` to confirm the CLI is wired up. If `evals/cases` is missing it will be cloned automatically from `discourse/discourse-ai-evals`.
+
+## Discover available inputs
+
+- `./run --list` lists all eval ids from `evals/cases/*/*.yml`.
+- `./run --list-features` prints feature keys grouped by module (format: `module:feature`).
+- `./run --list-models` shows LLM configs that can be hydrated from `eval-llms.yml`/`.local.yml`.
+- `./run --list-personas` lists persona keys defined under `evals/personas/*.yml` plus the built-in `default`.
+
+## Run evals
+
+- Run a single eval against specific models:
+
+  ```sh
+  OPENAI_API_KEY=... ./run --eval simple_summarization --models gpt-4o-mini
+  ```
+
+- Run every eval for a feature (or the whole suite) against multiple models:
+
+  ```sh
+  ./run --feature summarization:topic_summaries --models gpt-4o-mini,claude-3-5-sonnet-latest
+  ```
+
+  Omitting `--models` hydrates every configured LLM. Models that cannot hydrate (missing API keys, etc.) are skipped with a log message.
+
+- Some evals define a `judge` block. When any selected eval requires judging, the runner defaults to `--judge gpt-4o` unless you pass `--judge name`. Invalid or missing judge configs cause the CLI to exit before running.
+
+## Personas and comparison modes
+
+- Supply custom prompts with `--persona-keys key1,key2`. Keys resolve to YAML files in `evals/personas`; each needs `key` (optional, defaults to the filename), `system_prompt`, and an optional `description`.
+- Minimal persona example (`evals/personas/topic_summary_eval.yml`):
+
+  ```yml
+  key: topic_summary_eval
+  description: Variant tuned for eval comparisons
+  system_prompt: |
+    Summarize the topic in 2–4 sentences. Keep the original language and avoid new facts.
+  ```
+
+- `--compare personas` runs one model against multiple personas. The built-in `default` persona is automatically prepended so you can compare YAML prompts against stock behavior, and at least two personas are required.
+- `--compare llms` runs one persona (default unless overridden) across multiple models and asks the judge to score them side by side.
+- Non-comparison runs accept a single persona; pass one `--persona-keys` value or rely on the default prompt.
+
+## Dataset-driven runs
+
+- Generate eval cases from a CSV instead of YAML by passing `--dataset path/to/file.csv --feature module:feature`. The CSV must include `content` and `expected_output` columns; each row becomes its own eval id (`dataset-<filename>-<row>`).
+- Minimal CSV example:
+
+  ```csv
+  content,expected_output
+  "This is spam!!! Buy now!",true
+  "Genuine question about hosting",false
+  ```
+
+- Example:
+
+  ```sh
+  ./run --dataset evals/cases/spam/spam_eval_dataset.csv --feature spam:inspect_posts --models gpt-4o-mini
+  ```
+
+## Writing eval cases
+
+- Store cases under `evals/cases/<group>/<name>.yml`. Each file must declare `id`, `name`, `description`, and `feature` (the `module:feature` key registered with the plugin).
+- Provide inputs under `args`. Keys ending in `_path` (or `path`) are expanded relative to the YAML directory so you can reference fixture files. For multi-case files, `args` can contain arrays (for example, `cases:`) that runners iterate over.
+- Expected results can be declared with one of:
+  - `expected_output`: exact string match
+  - `expected_output_regex`: treated as a multiline regular expression
+  - `expected_tool_call`: expected tool invocation payload
+- Set `vision: true` for evals that require a vision-capable model. Include a `judge` section (`pass_rating`, `criteria`, and optional `label`) to have outputs scored by a judge LLM.
+
+## Results and logs
+
+- CLI output shows pass/fail per model and prints expected vs actual details on failures. Comparison runs also stream the judge’s winner and ratings.
+- Example pass/fail snippet:
+
+  ```
+  gpt-4o-mini: Passed 🟢
+  claude-3-5-sonnet-latest: Failed 🔴
+  ---- Expected ----
+  true
+  ---- Actual ----
+  false
+  ```
+
+- Comparison winner snippet:
+
+  ```
+  Comparing personas for topic-summary
+  Winner: topic_summary_eval
+  Reason: Captured key details and stayed concise.
+    - default: 7/10 — missed concrete use case
+    - topic_summary_eval: 9/10 — mentioned service dogs and tone was neutral
+  ```
+
+- Each run writes plain logs and structured traces to `plugins/discourse-ai/evals/log/` (timestamped `.log` and `.json` files). The JSON files are formatted for [ui.perfetto.dev](https://ui.perfetto.dev) to inspect the structured steps.
+- On completion the runner echoes the log paths; use them to audit skipped models, judge decisions, and raw outputs when iterating on prompts or features.
+
+## Common features (what to try first)
+
+- `summarization:topic_summaries`: Summarize a conversation.
+- `spam:inspect_posts`: Spam/ham classification.
+- `translation:topic_title_translator`: Translate topic titles while preserving tone/formatting.
+- `ai_helper:rewrite`: Prompt the AI helper for rewrites.
+- `tool_calls:tool_calls_with_no_tool` and `tool_calls:tool_call_chains`: Validate structured tool call behavior.