diff --git a/Readme.md b/Readme.md
index 34ad100..c492359 100644
--- a/Readme.md
+++ b/Readme.md
@@ -100,6 +100,55 @@
   fetchAll()
 ```
   
+### How big is my (virtual) corpus?
+
+`corpusStats` reports the size of the whole corpus or of a virtual corpus:
+
+```r
+library(RKorAPClient)
+kco <- KorAPConnection(verbose = TRUE)
+corpusStats(kco, vc = "pubDate since 2020")
+```
+
+```
+<KorAPCorpusStats>
+The virtual corpus described by "pubDate since 2020" contains 3,942,948,561 tokens
+in 253,752,552 sentences in 13,857,981 documents.
+```
+
+With `as.df = TRUE` you get a one row data frame with `tokens`, `sentences`, `paragraphs` and `documents` columns instead, which makes it easy to compare several virtual corpora.
+
+### Metadata of a text
+
+`textMetadata` retrieves all metadata KorAP holds for a text, given its sigle as found in the `textSigle` column of query results:
+
+```r
+KorAPConnection() |> textMetadata("WPD17/L79/98721")
+```
+
+|textSigle       |author                 |title    |pubDate    |textType     |
+|:---------------|:----------------------|:--------|:----------|:------------|
+|WPD17/L79/98721 |GeorgDerReisende, u.a. |Leverone |2017-07-01 |Enzyklopädie |
+
+The result has one column per metadata field the corpus provides – 26 in this example, so the table above shows only a selection.
+
+### Association scores for collocation candidates you already have
+
+While `collocationAnalysis` searches for collocates, `collocationScoreQuery` computes association scores for pairs you already have in mind. It accepts a vector of collocates and queries every combination of collocate and virtual corpus:
+
+```r
+KorAPConnection() |>
+  collocationScoreQuery("Grund", c("triftiger", "guter", "Berlin"))
+```
+
+|node  |collocate |        O|         E| logDice|   pmi|       ll|
+|:-----|:---------|--------:|---------:|-------:|-----:|--------:|
+|Grund |triftiger |  2390.06|      6.31|    0.64|  8.57| 26288.28|
+|Grund |guter     | 12902.28|   2713.05|    3.04|  2.25| 19938.66|
+|Grund |Berlin    |  7866.48|  26211.71|    2.03| -1.74| 17789.94|
+
+`O` is the observed and `E` the expected co-occurrence frequency. *Triftiger* is by far the most strongly attracted of the three (highest `pmi`), while *Berlin* co-occurs with *Grund* less often than chance would predict, which is what a negative `pmi` expresses.
+
 ### Identify *in … setzen* light verb constructions using `collocationAnalysis`
 
 ```r
diff --git a/tests/testthat/test-readme-against-llm.R b/tests/testthat/test-readme-against-llm.R
index 886ccf6..763ccde 100644
--- a/tests/testthat/test-readme-against-llm.R
+++ b/tests/testthat/test-readme-against-llm.R
@@ -369,4 +369,71 @@
       expect_true(execution_result, "Generated code should execute without runtime errors")
     }
   })
+
+  test_that(paste(model, "can solve corpus size task with README guidance"), {
+    skip_if_offline()
+    skip_if_no_api_key(model)
+    if (llmProvider(model)$name != "synthetic") skip_if_not_installed("tidyllm")
+    skip_if_not(!is.null(find_readme_path()), "Readme.md not found in current or parent directories")
+
+    prompt <- create_readme_prompt(
+      "write R code that reports how many tokens the virtual corpus of newspaper texts published since 2020 contains.",
+      "Write R code to determine the size of a virtual corpus using RKorAPClient."
+    )
+
+    generated_code <- extract_r_code(call_llm_api(prompt, model, max_tokens = 300))
+
+    expect_true(grepl("KorAPConnection", generated_code), "Generated code should include KorAPConnection")
+    expect_true(grepl("corpusStats", generated_code), "Generated code should include corpusStats")
+    expect_true(grepl("vc", generated_code), "Generated code should restrict to a virtual corpus")
+    expect_true(test_code_syntax(generated_code), "Generated code should be syntactically valid R code")
+
+    cat("Generated corpus size code:\n", generated_code, "\n")
+  })
+
+  test_that(paste(model, "can solve text metadata task with README guidance"), {
+    skip_if_offline()
+    skip_if_no_api_key(model)
+    if (llmProvider(model)$name != "synthetic") skip_if_not_installed("tidyllm")
+    skip_if_not(!is.null(find_readme_path()), "Readme.md not found in current or parent directories")
+
+    prompt <- create_readme_prompt(
+      "write R code that retrieves all metadata KorAP holds for the text with the sigle WPD17/L79/98721.",
+      "Write R code to retrieve the metadata of a text using RKorAPClient."
+    )
+
+    generated_code <- extract_r_code(call_llm_api(prompt, model, max_tokens = 300))
+
+    expect_true(grepl("KorAPConnection", generated_code), "Generated code should include KorAPConnection")
+    expect_true(grepl("textMetadata", generated_code), "Generated code should include textMetadata")
+    expect_true(grepl("WPD17/L79/98721", generated_code, fixed = TRUE), "Generated code should include the text sigle")
+    expect_true(test_code_syntax(generated_code), "Generated code should be syntactically valid R code")
+
+    cat("Generated text metadata code:\n", generated_code, "\n")
+  })
+
+  test_that(paste(model, "can solve association score task with README guidance"), {
+    skip_if_offline()
+    skip_if_no_api_key(model)
+    if (llmProvider(model)$name != "synthetic") skip_if_not_installed("tidyllm")
+    skip_if_not(!is.null(find_readme_path()), "Readme.md not found in current or parent directories")
+
+    prompt <- create_readme_prompt(
+      paste(
+        "write R code that computes association scores for the word 'Grund' together with each of the",
+        "collocates 'triftiger' and 'guter', without searching for collocates first."
+      ),
+      "Write R code to compute association scores for known collocation candidates using RKorAPClient."
+    )
+
+    generated_code <- extract_r_code(call_llm_api(prompt, model, max_tokens = 300))
+
+    expect_true(grepl("KorAPConnection", generated_code), "Generated code should include KorAPConnection")
+    expect_true(grepl("collocationScoreQuery", generated_code), "Generated code should include collocationScoreQuery")
+    expect_true(grepl("triftiger", generated_code), "Generated code should include the collocate 'triftiger'")
+    expect_true(grepl("guter", generated_code), "Generated code should include the collocate 'guter'")
+    expect_true(test_code_syntax(generated_code), "Generated code should be syntactically valid R code")
+
+    cat("Generated association score code:\n", generated_code, "\n")
+  })
 }