correct api ref in tools & cli evals docs

Author: montymxb

CHANGELOG.md

@@ -9,7 +9,7 @@ Big CLI update with breaking changes and an overall focus on improving stability
 - Support for multi-language Langium projects, previously we only handled the singular case
 - Added the `-y` flag to most commands in lai to accept defaults, allowing automatic setup in CI environments and as part of scripted workflows
 - Detection of outdated descriptors: loading a descriptor generated by an older LAI version now warns and recommends regenerating with `lai gen descriptor`
-- Detection of outdated `lai.config.jsonc` files (also prompts to regen with `lai init`)
+- Detection of outdated `lai.config.jsonc` files (also prompts to regen with `lai init config`)
 - Auto-summarization of validations over 1k lines in the default generated system prompt
 - Per-language validator detection: multi-language projects that add one validator per language are now picked up, and the generated system prompt adds a `Validation Rules` section for each
 

packages/cli/docs/evaluations.md

@@ -10,7 +10,7 @@ Evaluation cases are written as `.eval.ts` files using the vitest-style testing
 
 ```typescript
 import { describe, evaluation, beforeEach } from 'langium-ai-tools/evals';
-import type { EvalContext } from 'langium-ai-tools/testing';
+import type { EvalContext } from 'langium-ai-tools/evaluator';
 
 describe('Basic Code Generation', () => {
 

packages/langium-ai-tools/README.md

@@ -155,26 +155,26 @@ In general we stick to focusing on what Langium can do to help with evaluation,
 
 ### Testing API
 
-Langium AI Tools provides a vitest-style testing API for writing programmatic evaluation test suites. This allows you to define test cases in TypeScript with familiar features like:
+Langium AI Tools provides a vitest-style evaluation API for writing programmatic evaluation suites. This allows you to define evaluation cases in TypeScript with familiar features like:
 
-- **Test suites** with `describe()`, `describe.skip()`, and `describe.only()`
+- **Evaluation suites** with `describe()`, `describe.skip()`, and `describe.only()`
 - **Lifecycle hooks**: `beforeAll()`, `afterAll()`, `beforeEach()`, `afterEach()`
-- **Parametrized tests** with `evaluation.each()` for testing multiple data sets
-- **Test filtering** with `.skip()` and `.only()` modifiers
+- **Parametrized tests** with `evaluation.each()` for evaluating multiple data sets
+- **Evaluation filtering** with `.skip()` and `.only()` modifiers
 
 ```typescript
-import { describe, evaluation, beforeAll, afterAll, beforeEach } from 'langium-ai-tools/testing';
+import { describe, evaluation, beforeAll, afterAll, beforeEach } from 'langium-ai-tools/evals';
 
 describe('DSL Generation Tests', () => {
   let model;
 
   beforeAll(async () => {
-    // runs once before all tests
+    // runs once before all evals
     model = await setupModel();
   });
 
   beforeEach(() => {
-    // runs before each test
+    // runs before each eval
     clearCache();
   });
 
@@ -186,7 +186,7 @@ describe('DSL Generation Tests', () => {
     };
   });
 
-  // parametrized tests
+  // parametrized evals
   evaluation.each([
     { input: 'person Alice', expected: 'Alice' },
     { input: 'person Bob', expected: 'Bob' }
@@ -198,17 +198,17 @@ describe('DSL Generation Tests', () => {
   });
 
   afterAll(() => {
-    // cleanup after all tests
+    // cleanup after all evals
     cleanupModel();
   });
 });
 ```
 
-**For detailed documentation on the Testing API**, see the [Testing API Reference](../cli/docs/testing-api.md).
+**For detailed documentation on the Evaluation API**, see the [Evaluation API Reference](../cli/docs/evaluation-api.md).
 
 ### Evaluation Matrix
 
-The Evaluation Matrix provides a framework for testing multiple model configurations against a set of test cases using Langium AI evaluators. This is particularly helpful when comparing across models, prompt strategies, RAG setups, or other variations in your AI stack.
+The Evaluation Matrix provides a framework for checking multiple model configurations against a set of evaluation cases using Langium AI evaluators. This is particularly helpful when comparing across models, prompt strategies, RAG setups, or other variations in your AI stack.
 
 In practice an evaluation matrix can be helpful when deciding between which models or services to use up front, but this can also be done externally by levering the evaluator directly yourself.