diff --git a/python/mall/llm.py b/python/mall/llm.py
index efed256..41c3678 100644
--- a/python/mall/llm.py
+++ b/python/mall/llm.py
@@ -3,6 +3,7 @@
 import polars as pl
 import hashlib
 import ollama
+import copy
 import json
 import os
 
@@ -10,8 +11,9 @@
 def llm_use(backend="", model="", _cache="_mall_cache", **kwargs):
     out = dict()
     if isinstance(backend, Chat):
+        chat_copy = copy.deepcopy(backend)
         out.update(dict(backend="chatlas"))
-        out.update(dict(chat=backend))
+        out.update(dict(chat=chat_copy))
         backend = ""
         model = ""
     if isinstance(backend, Client):
@@ -42,6 +44,8 @@ def llm_map(df, col, msg, pred_name, use, valid_resps="", convert=None):
         pl_type = pl.Int8
         data_type = int
 
+    use = llm_init_use(use, msg)
+
     df = df.with_columns(
         pl.col(col)
         .map_elements(
@@ -61,9 +65,10 @@ def llm_map(df, col, msg, pred_name, use, valid_resps="", convert=None):
 
 
 def llm_loop(x, msg, use, valid_resps="", convert=None):
-    if isinstance(x, list) == False:
+    if not isinstance(x, list):
         raise TypeError("`x` is not a list object")
     out = list()
+    use = llm_init_use(use, msg)
     for row in x:
         out.append(
             llm_call(x=row, msg=msg, use=use, valid_resps=valid_resps, convert=convert)
@@ -71,8 +76,17 @@ def llm_loop(x, msg, use, valid_resps="", convert=None):
     return out
 
 
-def llm_call(x, msg, use, valid_resps="", convert=None, data_type=None):
+def llm_init_use(use, msg):
+    backend = use.get("backend")
+    if backend == "chatlas":
+        chat = use.get("chat")
+        chat.set_turns(list())
+        chat.system_prompt = msg
+        use.update(chat=chat)
+    return use
+
 
+def llm_call(x, msg, use, valid_resps="", convert=None, data_type=None):
     backend = use.get("backend")
     model = use.get("model")
     call = dict(
@@ -84,15 +98,13 @@ def llm_call(x, msg, use, valid_resps="", convert=None, data_type=None):
     out = ""
     cache = ""
     if use.get("_cache") != "":
-
         hash_call = build_hash(call)
         cache = cache_check(hash_call, use)
     if cache == "":
         if backend == "chatlas":
             chat = use.get("chat")
-            ch = chat.chat(msg[0].get("content") + x, echo="none")
+            ch = chat.chat(x, echo="none")
             out = ch.get_content()
-            chat.set_turns(list())
         if backend == "ollama" or backend == "ollama-client":
             if backend == "ollama":
                 chat_fun = ollama.chat
@@ -109,7 +121,7 @@ def llm_call(x, msg, use, valid_resps="", convert=None, data_type=None):
             if model == "echo":
                 out = x
             if model == "content":
-                out = msg[0]["content"]
+                out = msg
                 return out
     else:
         out = cache
@@ -143,10 +155,7 @@ def valid_output(x):
 
 
 def build_msg(x, msg):
-    out = []
-    for msgs in msg:
-        out.append({"role": msgs["role"], "content": msgs["content"].format(x)})
-    return out
+    return {'role': 'user', 'content': msg + str(x)}
 
 
 def build_hash(x):
diff --git a/python/mall/llmvec.py b/python/mall/llmvec.py
index 04ec047..88a9ecc 100644
--- a/python/mall/llmvec.py
+++ b/python/mall/llmvec.py
@@ -19,10 +19,11 @@ class LLMVec:
     from mall import LLMVec
 
     chat = ChatOllama(model = "llama3.2")
-    
-    llm = LLMVec(chat)    
+
+    llm = LLMVec(chat)
     ```
     """
+
     def __init__(self, backend="", model="", _cache="_mall_cache", **kwargs):
         self._use = llm_use(backend=backend, model=model, _cache=_cache, **kwargs)
 
@@ -49,10 +50,10 @@ def sentiment(
         ```{python}
         llm.sentiment(['I am happy', 'I am sad'])
         ```
-        """    
+        """
         return llm_loop(
             x=x,
-            msg=sentiment(options, additional=additional),
+            msg=sentiment(options, additional=additional, use=self._use),
             use=self._use,
             valid_resps=options,
         )
@@ -77,10 +78,10 @@ def summarize(self, x, max_words=10, additional="") -> list:
         ```{python}
         llm.summarize(['This has been the best TV Ive ever used. Great screen, and sound.'], max_words = 5)
         ```
-        """        
+        """
         return llm_loop(
             x=x,
-            msg=summarize(max_words, additional=additional),
+            msg=summarize(max_words, additional=additional, use=self._use),
             use=self._use,
         )
 
@@ -106,10 +107,10 @@ def translate(self, x, language="", additional="") -> list:
         llm.translate(['This has been the best TV Ive ever used. Great screen, and sound.'], language = 'spanish')
         ```
 
-        """        
+        """
         return llm_loop(
             x=x,
-            msg=translate(language, additional=additional),
+            msg=translate(language, additional=additional, use=self._use),
             use=self._use,
         )
 
@@ -135,10 +136,10 @@ def classify(self, x, labels="", additional="") -> list:
         ```{python}
         llm.classify(['this is important!', 'there is no rush'], ['urgent', 'not urgent'])
         ```
-        """        
+        """
         return llm_loop(
             x=x,
-            msg=classify(labels, additional=additional),
+            msg=classify(labels, additional=additional, use=self._use),
             use=self._use,
             valid_resps=labels,
         )
@@ -164,8 +165,12 @@ def extract(self, x, labels="", additional="") -> list:
         ```{python}
         llm.extract(['bob smith, 123 3rd street'], labels=['name', 'address'])
         ```
-        """        
-        return llm_loop(x=x, msg=extract(labels, additional=additional), use=self._use)
+        """
+        return llm_loop(
+            x=x,
+            msg=extract(labels, additional=additional, use=self._use),
+            use=self._use,
+        )
 
     def custom(self, x, prompt="", valid_resps="") -> list:
         """Provide the full prompt that the LLM will process.
@@ -178,7 +183,7 @@ def custom(self, x, prompt="", valid_resps="") -> list:
         prompt : str
             The prompt to send to the LLM along with the `col`
 
-        """        
+        """
         return llm_loop(x=x, msg=custom(prompt), use=self._use, valid_resps=valid_resps)
 
     def verify(self, x, what="", yes_no=[1, 0], additional="") -> list:
@@ -201,10 +206,10 @@ def verify(self, x, what="", yes_no=[1, 0], additional="") -> list:
         additional : str
             Inserts this text into the prompt sent to the LLM
 
-        """        
+        """
         return llm_loop(
             x=x,
-            msg=verify(what, additional=additional),
+            msg=verify(what, additional=additional, use=self._use),
             use=self._use,
             valid_resps=yes_no,
             convert=dict(yes=yes_no[0], no=yes_no[1]),
diff --git a/python/mall/polars.py b/python/mall/polars.py
index d7ad452..d4cb725 100644
--- a/python/mall/polars.py
+++ b/python/mall/polars.py
@@ -150,7 +150,7 @@ def sentiment(
         df = llm_map(
             df=self._df,
             col=col,
-            msg=sentiment(options, additional=additional),
+            msg=sentiment(options, additional=additional, use=self._use),
             pred_name=pred_name,
             use=self._use,
             valid_resps=options,
@@ -197,7 +197,7 @@ def summarize(
         df = llm_map(
             df=self._df,
             col=col,
-            msg=summarize(max_words, additional=additional),
+            msg=summarize(max_words, additional=additional, use=self._use),
             pred_name=pred_name,
             use=self._use,
         )
@@ -243,7 +243,7 @@ def translate(
         df = llm_map(
             df=self._df,
             col=col,
-            msg=translate(language, additional=additional),
+            msg=translate(language, additional=additional, use=self._use),
             pred_name=pred_name,
             use=self._use,
         )
@@ -295,7 +295,7 @@ def classify(
         df = llm_map(
             df=self._df,
             col=col,
-            msg=classify(labels, additional=additional),
+            msg=classify(labels, additional=additional, use=self._use),
             pred_name=pred_name,
             use=self._use,
             valid_resps=labels,
@@ -379,7 +379,7 @@ def extract(
         df = llm_map(
             df=self._df,
             col=col,
-            msg=extract(lab_vals, additional=additional),
+            msg=extract(lab_vals, additional=additional, use=self._use),
             pred_name=pred_name,
             use=self._use,
         )
@@ -484,7 +484,7 @@ def verify(
         df = llm_map(
             df=self._df,
             col=col,
-            msg=verify(what, additional=additional),
+            msg=verify(what, additional=additional, use=self._use),
             pred_name=pred_name,
             use=self._use,
             valid_resps=yes_no,
diff --git a/python/mall/prompt.py b/python/mall/prompt.py
index d477813..968f298 100644
--- a/python/mall/prompt.py
+++ b/python/mall/prompt.py
@@ -1,72 +1,54 @@
-def sentiment(options, additional=""):
+def sentiment(options, additional="", use=[]):
     new_options = process_labels(
         options,
         "Return only one of the following answers: {values} ",
         "- If the text is {key}, return {value} ",
     )
-    msg = [
-        {
-            "role": "user",
-            "content": "You are a helpful sentiment engine. "
-            + f"{new_options}. "
-            + "No capitalization. No explanations. "
-            + f"{additional} "
-            + "The answer is based on the following text:\n{}",
-        }
-    ]
-    return msg
-
-
-def summarize(max_words, additional=""):
-    msg = [
-        {
-            "role": "user",
-            "content": "You are a helpful summarization engine. "
-            + "Your answer will contain no no capitalization and no explanations. "
-            + f"Return no more than "
-            + str(max_words)
-            + " words. "
-            + f" {additional} "
-            + "The answer is the summary of the following text:\n{}",
-        }
-    ]
-    return msg
-
-
-def translate(language, additional=""):
-    msg = [
-        {
-            "role": "user",
-            "content": "You are a helpful translation engine. "
-            + "You will return only the translation text, no explanations. "
-            + f"The target language to translate to is: {language}. "
-            + f" {additional} "
-            + "The answer is the translation of the following text:\n{}",
-        }
-    ]
-    return msg
-
-
-def classify(labels, additional=""):
-    new_labels = process_labels(
+    x = (
+        "You are a helpful sentiment engine."
+        f" {new_options}. "
+        " No capitalization. No explanations."
+        f" {additional}"
+    )
+    return prompt_complete(x, use)
+
+
+def summarize(max_words, additional="", use=[]):
+    x = (
+        "You are a helpful summarization engine. "
+        "Your answer will contain no capitalization and no explanations. "
+        f"Return no more than {max_words} words. "
+        f"{additional}"
+    )
+    return prompt_complete(x, use)
+
+
+def translate(language, additional="", use=[]):
+    x = (
+        "You are a helpful translation engine. "
+        "You will return only the translation text, no explanations. "
+        f"The target language to translate to is: {language}. "
+        f"{additional}"
+    )
+    return prompt_complete(x, use)
+
+
+def classify(labels, additional="", use=[]):
+    labels = process_labels(
         labels,
         "Determine if the text refers to one of the following:{values} ",
         "- If the text is {key}, return {value} ",
     )
-    msg = [
-        {
-            "role": "user",
-            "content": "You are a helpful classification engine. "
-            + f"{new_labels}. "
-            + "No capitalization. No explanations. "
-            + f"{additional} "
-            + "The answer is based on the following text:\n{}",
-        }
-    ]
-    return msg
-
-
-def extract(labels, additional=""):
+    x = (
+        "You are a helpful classification engine. "
+        f"{labels}. "
+        "No capitalization. No explanations. "
+        f"{additional}"
+    )
+    return prompt_complete(x, use)
+
+
+def extract(labels, additional="", use=[]):
     col_labels = ""
     if isinstance(labels, list):
         no_labels = len(labels)
@@ -84,39 +66,30 @@ def extract(labels, additional=""):
         text_multi = ""
         col_labels = labels
 
-    msg = [
-        {
-            "role": "user",
-            "content": "You are a helpful text extraction engine. "
-            + f"Extract the {col_labels} being referred to on the text. "
-            + f"I expect {no_labels} item{plural} exactly. "
-            + "No capitalization. No explanations. "
-            + f" {text_multi} "
-            + f" {additional} "
-            + "The answer is based on the following text:\n{}",
-        }
-    ]
-    return msg
-
-
-def verify(what, additional=""):
-    msg = [
-        {
-            "role": "user",
-            "content": "You are a helpful text analysis engine. "
-            + "Determine this is true "
-            + f"'{what}'."
-            + "No capitalization. No explanations. "
-            + f"{additional} "
-            + "The answer is based on the following text:\n{}",
-        }
-    ]
-    return msg
+    x = (
+        "You are a helpful text extraction engine. "
+        f"Extract the {col_labels} being referred to in the text. "
+        f"I expect {no_labels} item{plural} exactly. "
+        "No capitalization. No explanations. "
+        f"{text_multi}"
+        f"{additional}"
+    )
+    return prompt_complete(x, use)
+
+
+def verify(what, additional="", use=[]):
+    x = (
+        "You are a helpful text analysis engine."
+        "Determine if this is true "
+        f"'{what}'."
+        "No capitalization. No explanations."
+        f"{additional}"
+    )
+    return prompt_complete(x, use)
 
 
 def custom(prompt):
-    msg = [{"role": "user", "content": f"{prompt}" + ": \n{}"}]
-    return msg
+    return prompt
 
 
 def process_labels(x, if_list="", if_dict=""):
@@ -135,3 +108,16 @@ def process_labels(x, if_list="", if_dict=""):
             new = new.replace("{value}", str(x.get(i)))
             out += " " + new
     return out
+
+
+def prompt_complete(x, use):
+    backend = use.get("backend")
+    if backend == "chatlas":
+        x = (
+            x
+            + "The answer will be based on each individual prompt."
+            + " Treat each prompt as unique when deciding the answer."
+        )
+    else:
+        x = x + "The answer is based on the following text:\n{}"
+    return x
diff --git a/python/pyproject.toml b/python/pyproject.toml
index f4b4d2d..04e1b48 100644
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -3,7 +3,7 @@ packages = ["mall"]
 
 [project]
 name = "mlverse-mall"
-version = "0.1.0.9003"
+version = "0.1.0.9004"
 description = "Run multiple 'Large Language Model' predictions against a table. The predictions run row-wise over a specified column."
 readme = "README.md"
 authors = [
diff --git a/python/tests/test_custom.py b/python/tests/test_custom.py
index efca829..33ba7f8 100644
--- a/python/tests/test_custom.py
+++ b/python/tests/test_custom.py
@@ -9,7 +9,7 @@ def test_custom_prompt():
     df = pl.DataFrame(dict(x="x"))
     df.llm.use("test", "content", _cache="_test_cache")
     x = df.llm.custom("x", "hello")
-    assert x["custom"][0] == "hello: \n{}"
+    assert x["custom"][0] == "hello"
     shutil.rmtree("_test_cache", ignore_errors=True)
 
 def test_custom_vec():
diff --git a/python/tests/test_extract.py b/python/tests/test_extract.py
index 9db3f86..3d99524 100644
--- a/python/tests/test_extract.py
+++ b/python/tests/test_extract.py
@@ -11,7 +11,7 @@ def test_extract_list():
     x = df.llm.extract("x", ["a", "b"])
     assert (
         x["extract"][0]
-        == "You are a helpful text extraction engine. Extract the a, b being referred to on the text. I expect 2 items exactly. No capitalization. No explanations.  Return the response exclusively in a pipe separated list, and no headers.    The answer is based on the following text:\n{}"
+        == "You are a helpful text extraction engine. Extract the a, b being referred to in the text. I expect 2 items exactly. No capitalization. No explanations. Return the response exclusively in a pipe separated list, and no headers. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
@@ -22,7 +22,7 @@ def test_extract_dict():
     x = df.llm.extract("x", dict(a="one", b="two"))
     assert (
         x["extract"][0]
-        == "You are a helpful text extraction engine. Extract the one, two being referred to on the text. I expect 2 items exactly. No capitalization. No explanations.  Return the response exclusively in a pipe separated list, and no headers.    The answer is based on the following text:\n{}"
+        == "You are a helpful text extraction engine. Extract the one, two being referred to in the text. I expect 2 items exactly. No capitalization. No explanations. Return the response exclusively in a pipe separated list, and no headers. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
@@ -33,7 +33,7 @@ def test_extract_one():
     x = df.llm.extract("x", labels="a")
     assert (
         x["extract"][0]
-        == "You are a helpful text extraction engine. Extract the a being referred to on the text. I expect 1 item exactly. No capitalization. No explanations.     The answer is based on the following text:\n{}"
+        == "You are a helpful text extraction engine. Extract the a being referred to in the text. I expect 1 item exactly. No capitalization. No explanations. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
diff --git a/python/tests/test_sentiment.py b/python/tests/test_sentiment.py
index 1982acf..b6bd0fe 100644
--- a/python/tests/test_sentiment.py
+++ b/python/tests/test_sentiment.py
@@ -38,7 +38,7 @@ def test_sentiment_prompt():
     x = df.llm.sentiment("x")
     assert (
         x["sentiment"][0]
-        == "You are a helpful sentiment engine. Return only one of the following answers: positive, negative, neutral . No capitalization. No explanations.  The answer is based on the following text:\n{}"
+        == "You are a helpful sentiment engine. Return only one of the following answers: positive, negative, neutral .  No capitalization. No explanations. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
diff --git a/python/tests/test_summarize.py b/python/tests/test_summarize.py
index 5efc3c4..68c72cb 100644
--- a/python/tests/test_summarize.py
+++ b/python/tests/test_summarize.py
@@ -11,7 +11,7 @@ def test_summarize_prompt():
     x = df.llm.summarize("x")
     assert (
         x["summary"][0]
-        == "You are a helpful summarization engine. Your answer will contain no no capitalization and no explanations. Return no more than 10 words.   The answer is the summary of the following text:\n{}"
+        == "You are a helpful summarization engine. Your answer will contain no capitalization and no explanations. Return no more than 10 words. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
@@ -22,7 +22,7 @@ def test_summarize_max():
     x = df.llm.summarize("x", max_words=5)
     assert (
         x["summary"][0]
-        == "You are a helpful summarization engine. Your answer will contain no no capitalization and no explanations. Return no more than 5 words.   The answer is the summary of the following text:\n{}"
+        == "You are a helpful summarization engine. Your answer will contain no capitalization and no explanations. Return no more than 5 words. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
diff --git a/python/tests/test_translate.py b/python/tests/test_translate.py
index 88a251c..87b33eb 100644
--- a/python/tests/test_translate.py
+++ b/python/tests/test_translate.py
@@ -11,7 +11,7 @@ def test_translate_prompt():
     x = df.llm.translate("x", language="spanish")
     assert (
         x["translation"][0]
-        == "You are a helpful translation engine. You will return only the translation text, no explanations. The target language to translate to is: spanish.   The answer is the translation of the following text:\n{}"
+        == "You are a helpful translation engine. You will return only the translation text, no explanations. The target language to translate to is: spanish. The answer is based on the following text:\n{}"
     )
     shutil.rmtree("_test_cache", ignore_errors=True)
 
diff --git a/r/DESCRIPTION b/r/DESCRIPTION
index a46f57d..229d593 100644
--- a/r/DESCRIPTION
+++ b/r/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: mall
 Title: Run Multiple Large Language Model Predictions Against a Table, or 
     Vectors
-Version: 0.1.0.9003
+Version: 0.1.0.9004
 Authors@R: c(
   person("Edgar", "Ruiz", , "edgar@posit.co", role = c("aut", "cre")),
   person(given = "Posit Software, PBC", role = c("cph", "fnd"))
diff --git a/r/NAMESPACE b/r/NAMESPACE
index 3903ec9..2d2244d 100644
--- a/r/NAMESPACE
+++ b/r/NAMESPACE
@@ -10,7 +10,9 @@ S3method(llm_summarize,"tbl_Spark SQL")
 S3method(llm_summarize,data.frame)
 S3method(llm_translate,data.frame)
 S3method(llm_verify,data.frame)
+S3method(m_backend_prompt,mall_ellmer)
 S3method(m_backend_prompt,mall_llama3.2)
+S3method(m_backend_prompt,mall_ollama)
 S3method(m_backend_prompt,mall_session)
 S3method(m_backend_submit,mall_ellmer)
 S3method(m_backend_submit,mall_ollama)
diff --git a/r/R/m-backend-prompt.R b/r/R/m-backend-prompt.R
index b0e09d3..d38263f 100644
--- a/r/R/m-backend-prompt.R
+++ b/r/R/m-backend-prompt.R
@@ -4,10 +4,31 @@ m_backend_prompt <- function(backend, additional) {
   UseMethod("m_backend_prompt")
 }
 
+#' @export
+m_backend_prompt.mall_ollama <- function(backend, additional = "") {
+  next_method <- NextMethod()
+  additional <- glue(paste(
+    additional,
+    "The answer is based on the following text:\n{{x}}"
+  ))
+  next_method
+}
+
+#' @export
+m_backend_prompt.mall_ellmer <- function(backend, additional = "") {
+  next_method <- NextMethod()
+  additional <- glue(paste(
+    additional,
+    "The answer will be based on each individual prompt.",
+    "Treat each prompt as unique when deciding the answer."
+  ))
+  next_method
+}
+
 #' @export
 m_backend_prompt.mall_llama3.2 <- function(backend, additional = "") {
-  base_method <- NextMethod()
-  base_method$extract <- function(labels) {
+  next_method <- NextMethod()
+  next_method$extract <- function(labels) {
     no_labels <- length(labels)
     col_labels <- paste0(labels, collapse = ", ")
     plural <- ifelse(no_labels > 1, "s", "")
@@ -16,41 +37,29 @@ m_backend_prompt.mall_llama3.2 <- function(backend, additional = "") {
       "Return the response exclusively in a pipe separated list, and no headers. ",
       ""
     )
-    list(
-      list(
-        role = "user",
-        content = glue(paste(
-          "You are a helpful text extraction engine.",
-          "Extract the {col_labels} being referred to in the text.",
-          "I expect {no_labels} item{plural} exactly.",
-          "No capitalization. No explanations.",
-          "{text_multi}",
-          "{additional}",
-          "The answer is based on the following text:\n{{x}}"
-        ))
-      )
-    )
+    glue(paste(
+      "You are a helpful text extraction engine.",
+      "Extract the {col_labels} being referred to in the text.",
+      "I expect {no_labels} item{plural} exactly.",
+      "No capitalization. No explanations.",
+      "{text_multi}",
+      "{additional}"
+    ))
   }
-  base_method$classify <- function(labels) {
+  next_method$classify <- function(labels) {
     labels <- process_labels(
       x = labels,
       if_character = "Determine if the text refers to one of the following: {x}",
       if_formula = "If it classifies as {f_lhs(x)} then return {f_rhs(x)}"
     )
-    list(
-      list(
-        role = "user",
-        content = glue(paste(
-          "You are a helpful classification engine.",
-          "{labels}.",
-          "No capitalization. No explanations.",
-          "{additional}",
-          "The answer is based on the following text:\n{{x}}"
-        ))
-      )
-    )
+    glue(paste(
+      "You are a helpful classification engine.",
+      "{labels}.",
+      "No capitalization. No explanations.",
+      "{additional}"
+    ))
   }
-  base_method
+  next_method
 }
 
 #' @export
@@ -62,32 +71,20 @@ m_backend_prompt.mall_session <- function(backend, additional = "") {
         if_character = "Return only one of the following answers: {x}",
         if_formula = "- If the text is {f_lhs(x)}, return {f_rhs(x)}"
       )
-      list(
-        list(
-          role = "user",
-          content = glue(paste(
-            "You are a helpful sentiment engine.",
-            "{options}.",
-            "No capitalization. No explanations.",
-            "{additional}",
-            "The answer is based on the following text:\n{{x}}"
-          ))
-        )
-      )
+      glue(paste(
+        "You are a helpful sentiment engine.",
+        "{options}.",
+        "No capitalization. No explanations.",
+        "{additional}"
+      ))
     },
     summarize = function(max_words) {
-      list(
-        list(
-          role = "user",
-          content = glue(paste(
-            "You are a helpful summarization engine.",
-            "Your answer will contain no capitalization and no explanations.",
-            "Return no more than {max_words} words.",
-            "{additional}",
-            "The answer is the summary of the following text:\n{{x}}"
-          ))
-        )
-      )
+      glue(paste(
+        "You are a helpful summarization engine.",
+        "Your answer will contain no capitalization and no explanations.",
+        "Return no more than {max_words} words.",
+        "{additional}"
+      ))
     },
     classify = function(labels) {
       labels <- process_labels(
@@ -95,18 +92,12 @@ m_backend_prompt.mall_session <- function(backend, additional = "") {
         if_character = "Determine if the text refers to one of the following: {x}",
         if_formula = "- For {f_lhs(x)}, return {f_rhs(x)}"
       )
-      list(
-        list(
-          role = "user",
-          content = glue(paste(
-            "You are a helpful classification engine.",
-            "{labels}.",
-            "No capitalization. No explanations.",
-            "{additional}",
-            "The answer is based on the following text:\n{{x}}"
-          ))
-        )
-      )
+      glue(paste(
+        "You are a helpful classification engine.",
+        "{labels}.",
+        "No capitalization. No explanations.",
+        "{additional}"
+      ))
     },
     extract = function(labels) {
       no_labels <- length(labels)
@@ -117,49 +108,31 @@ m_backend_prompt.mall_session <- function(backend, additional = "") {
         "Return the response in a simple list, pipe separated, and no headers. ",
         ""
       )
-      list(
-        list(
-          role = "user",
-          content = glue(paste(
-            "You are a helpful text extraction engine.",
-            "Extract the {col_labels} being referred to in the text.",
-            "I expect {no_labels} item{plural} exactly.",
-            "No capitalization. No explanations.",
-            "{text_multi}",
-            "{additional}",
-            "The answer is based on the following text:\n{{x}}"
-          ))
-        )
-      )
+      glue(paste(
+        "You are a helpful text extraction engine.",
+        "Extract the {col_labels} being referred to in the text.",
+        "I expect {no_labels} item{plural} exactly.",
+        "No capitalization. No explanations.",
+        "{text_multi}",
+        "{additional}"
+      ))
     },
     translate = function(language) {
-      list(
-        list(
-          role = "user",
-          content = glue(paste(
-            "You are a helpful translation engine.",
-            "You will return only the translation text, no explanations.",
-            "The target language to translate to is: {language}.",
-            "{additional}",
-            "The answer is the summary of the following text:\n{{x}}"
-          ))
-        )
-      )
+      glue(paste(
+        "You are a helpful translation engine.",
+        "You will return only the translation text, no explanations.",
+        "The target language to translate to is: {language}.",
+        "{additional}"
+      ))
     },
     verify = function(what, labels) {
-      list(
-        list(
-          role = "user",
-          content = glue(paste(
-            "You are a helpful text analysis engine.",
-            "Determine if this is true ",
-            "'{what}'.",
-            "No capitalization. No explanations.",
-            "{additional}",
-            "The answer is based on the following text:\n{{x}}"
-          ))
-        )
-      )
+      glue(paste(
+        "You are a helpful text analysis engine.",
+        "Determine if this is true ",
+        "'{what}'.",
+        "No capitalization. No explanations.",
+        "{additional}"
+      ))
     }
   )
 }
diff --git a/r/R/m-backend-submit.R b/r/R/m-backend-submit.R
index 3a0dd00..d132df7 100644
--- a/r/R/m-backend-submit.R
+++ b/r/R/m-backend-submit.R
@@ -19,6 +19,9 @@ m_backend_submit <- function(backend, x, prompt, preview = FALSE) {
 
 #' @export
 m_backend_submit.mall_ollama <- function(backend, x, prompt, preview = FALSE) {
+  prompt <- list(
+    list(role = "user", content = prompt)
+  )
   if (preview) {
     x <- head(x, 1)
     map_here <- map
@@ -89,22 +92,17 @@ m_ollama_tokens <- function() {
 
 #' @export
 m_backend_submit.mall_ellmer <- function(backend, x, prompt, preview = FALSE) {
-  # Treats prompt as a system prompt
-  system_prompt <- prompt[[1]][["content"]]
-  system_prompt <- glue(system_prompt, x = "")
-  # Returns two expressions if on preview: setting the system prompt and the
-  # first chat call
   if (preview) {
     return(
       exprs(
-        ellmer_obj$set_system_prompt(!!system_prompt),
+        ellmer_obj$set_system_prompt(!!prompt),
         ellmer_obj$chat(as.list(!!head(x, 1)))
       )
     )
   }
   ellmer_obj <- backend[["args"]][["ellmer_obj"]]
   if (m_cache_use()) {
-    hashed_x <- map(x, function(x) hash(c(ellmer_obj, system_prompt, x)))
+    hashed_x <- map(x, function(x) hash(c(ellmer_obj, prompt, x)))
     from_cache <- map(hashed_x, m_cache_check)
     null_cache <- map_lgl(from_cache, is.null)
     x <- x[null_cache]
@@ -112,14 +110,14 @@ m_backend_submit.mall_ellmer <- function(backend, x, prompt, preview = FALSE) {
   from_llm <- NULL
   if (length(x) > 0) {
     temp_ellmer <- ellmer_obj$clone()$set_turns(list())
-    temp_ellmer$set_system_prompt(system_prompt)
+    temp_ellmer$set_system_prompt(prompt)
     from_llm <- parallel_chat_text(temp_ellmer, as.list(x))
   }
   if (m_cache_use()) {
     walk(
       seq_along(from_llm),
       function(y) {
-        m_cache_record(list(system_prompt, x[y]), from_llm[y], hashed_x[y])
+        m_cache_record(list(prompt, x[y]), from_llm[y], hashed_x[y])
       }
     )
     res <- rep("", times = length(null_cache))
@@ -127,8 +125,9 @@ m_backend_submit.mall_ellmer <- function(backend, x, prompt, preview = FALSE) {
     res[!null_cache] <- from_cache[!null_cache]
     res
   } else {
-    from_llm
+    res <- from_llm
   }
+  map_chr(res, ~.x)
 }
 
 # Using a function so that it can be mocked in testing
diff --git a/r/R/m-defaults.R b/r/R/m-defaults.R
index 383850f..0749658 100644
--- a/r/R/m-defaults.R
+++ b/r/R/m-defaults.R
@@ -13,9 +13,9 @@ m_defaults_set <- function(...) {
     sub_model <- NULL
   }
   obj_class <- clean_names(c(
+    defaults[["backend"]],
     model,
     sub_model,
-    defaults[["backend"]],
     "session"
   ))
   .env_llm$defaults <- defaults
diff --git a/r/R/m-vec-prompt.R b/r/R/m-vec-prompt.R
index ba69590..03f390e 100644
--- a/r/R/m-vec-prompt.R
+++ b/r/R/m-vec-prompt.R
@@ -18,19 +18,6 @@ m_vec_prompt <- function(x,
     fn <- defaults[[prompt_label]]
     prompt <- fn(...)
   }
-  # If the prompt is a character, it will convert it to
-  # a list so it can be processed
-  if (!inherits(prompt, "list")) {
-    p_split <- strsplit(prompt, "\\{\\{x\\}\\}")[[1]]
-    if (length(p_split) == 1 && p_split == prompt) {
-      content <- glue("{prompt}\n{{x}}")
-    } else {
-      content <- prompt
-    }
-    prompt <- list(
-      list(role = "user", content = content)
-    )
-  }
   # Submits final prompt to the LLM
   resp <- m_backend_submit(
     backend = backend,
diff --git a/r/tests/testthat/_snaps/llm-classify.md b/r/tests/testthat/_snaps/llm-classify.md
index 980521d..b94a21f 100644
--- a/r/tests/testthat/_snaps/llm-classify.md
+++ b/r/tests/testthat/_snaps/llm-classify.md
@@ -12,7 +12,7 @@
     Code
       llm_vec_classify("this is a test", c("a", "b"), preview = TRUE)
     Output
-      ollamar::chat(messages = list(list(role = "user", content = "You are a helpful classification engine. Determine if the text refers to one of the following: a, b. No capitalization. No explanations.  The answer is based on the following text:\nthis is a test")), 
+      ollamar::chat(messages = list(list(role = "user", content = "You are a helpful classification engine. Determine if the text refers to one of the following: a, b. No capitalization. No explanations. The answer is based on the following text:\nthis is a test")), 
           output = "text", model = "llama3.2", seed = 100)
 
 # Classify on Ollama works
diff --git a/r/tests/testthat/_snaps/llm-custom.md b/r/tests/testthat/_snaps/llm-custom.md
index 5e1342a..c658a62 100644
--- a/r/tests/testthat/_snaps/llm-custom.md
+++ b/r/tests/testthat/_snaps/llm-custom.md
@@ -8,7 +8,7 @@
       2          I regret buying this laptop. It is too slow and the keyboard is too noisy
       3 Not sure how to feel about my new washing machine. Great color, but hard to figure
         .pred
-      1   Yes
+      1    No
       2    No
       3    No
 
diff --git a/r/tests/testthat/_snaps/llm-extract.md b/r/tests/testthat/_snaps/llm-extract.md
index 452e6f6..68bdb5f 100644
--- a/r/tests/testthat/_snaps/llm-extract.md
+++ b/r/tests/testthat/_snaps/llm-extract.md
@@ -3,15 +3,7 @@
     Code
       llm_vec_extract("toaster", labels = "product")
     Output
-      [[1]]
-      [[1]]$role
-      [1] "user"
-      
-      [[1]]$content
-      You are a helpful text extraction engine. Extract the product being referred to in the text. I expect 1 item exactly. No capitalization. No explanations.   The answer is based on the following text:
-      {x}
-      
-      
+      You are a helpful text extraction engine. Extract the product being referred to in the text. I expect 1 item exactly. No capitalization. No explanations.  
 
 # Extract on Ollama works
 
diff --git a/r/tests/testthat/_snaps/llm-summarize.md b/r/tests/testthat/_snaps/llm-summarize.md
index a1b9aeb..86e44eb 100644
--- a/r/tests/testthat/_snaps/llm-summarize.md
+++ b/r/tests/testthat/_snaps/llm-summarize.md
@@ -25,8 +25,8 @@
       1                 This has been the best TV I've ever used. Great screen, and sound.
       2          I regret buying this laptop. It is too slow and the keyboard is too noisy
       3 Not sure how to feel about my new washing machine. Great color, but hard to figure
-                              .summary
-      1    great tv with good features
-      2  laptop purchase was a mistake
-      3 having mixed feelings about it
+                            .summary
+      1 this tv is excellent quality
+      2  i regret my laptop purchase
+      3  confused about the purchase
 
diff --git a/r/tests/testthat/_snaps/llm-translate.md b/r/tests/testthat/_snaps/llm-translate.md
index e589ae2..46bc0ff 100644
--- a/r/tests/testthat/_snaps/llm-translate.md
+++ b/r/tests/testthat/_snaps/llm-translate.md
@@ -7,8 +7,8 @@
       1                 This has been the best TV I've ever used. Great screen, and sound.
       2          I regret buying this laptop. It is too slow and the keyboard is too noisy
       3 Not sure how to feel about my new washing machine. Great color, but hard to figure
-                                                                                              .translation
-      1                         Esta ha sido la mejor televisión que he utilizado. Gran pantalla y sonido.
-      2        Me arrepiento de comprar este portátil. Es demasiado lento y la tecla es demasiado ruidosa.
-      3 No estoy seguro de cómo me siento sobre mi nueva lavadora. Un gran color, pero difícil de entender
+                                                                                             .translation
+      1            Esta ha sido la mejor televisión que he utilizado hasta ahora. Gran pantalla y sonido.
+      2           Lo lamento comprar este portátil. Es demasiado lento y el teclado es demasiado ruidoso.
+      3 No estoy seguro de cómo sentirme con mi nueva lavadora. Un color grande, pero difícil de entender
 
diff --git a/r/tests/testthat/_snaps/llm-verify.md b/r/tests/testthat/_snaps/llm-verify.md
index 33910e0..9267220 100644
--- a/r/tests/testthat/_snaps/llm-verify.md
+++ b/r/tests/testthat/_snaps/llm-verify.md
@@ -3,7 +3,7 @@
     Code
       llm_vec_verify("this is a test", "a test", preview = TRUE)
     Output
-      ollamar::chat(messages = list(list(role = "user", content = "You are a helpful text analysis engine. Determine if this is true  'a test'. No capitalization. No explanations.  The answer is based on the following text:\nthis is a test")), 
+      ollamar::chat(messages = list(list(role = "user", content = "You are a helpful text analysis engine. Determine if this is true  'a test'. No capitalization. No explanations. The answer is based on the following text:\nthis is a test")), 
           output = "text", model = "llama3.2", seed = 100)
 
 # Verify on Ollama works
diff --git a/r/tests/testthat/_snaps/m-backend-submit.md b/r/tests/testthat/_snaps/m-backend-submit.md
index 00a92bb..4aaa172 100644
--- a/r/tests/testthat/_snaps/m-backend-submit.md
+++ b/r/tests/testthat/_snaps/m-backend-submit.md
@@ -29,7 +29,7 @@
         content = "this is the prompt")), preview = TRUE)
     Output
       [[1]]
-      ellmer_obj$set_system_prompt("this is the prompt")
+      ellmer_obj$set_system_prompt(list(list(content = "this is the prompt")))
       
       [[2]]
       ellmer_obj$chat(as.list("this is x"))
diff --git a/r/tests/testthat/test-m-backend-prompt.R b/r/tests/testthat/test-m-backend-prompt.R
index 402dc1e..60dca1a 100644
--- a/r/tests/testthat/test-m-backend-prompt.R
+++ b/r/tests/testthat/test-m-backend-prompt.R
@@ -9,7 +9,7 @@ test_that("Prompt handles list()", {
   test_text <- "Custom:{prompt}\n{{x}}"
   expect_equal(
     llm_vec_custom(x = "new test", prompt = test_text),
-    list(list(role = "user", content = test_text))
+    test_text
   )
 })
 
@@ -22,6 +22,6 @@ test_that("Prompt handles list()", {
   y <- m_backend_prompt(backend)
   y_extract <- y$extract(labels = c("a", "b"))
   y_classify <- y$classify(labels = c("a" ~ 1, "b" ~ 2))
-  expect_false(x_extract[[1]]$content == y_extract[[1]]$content)
-  expect_false(x_classify[[1]]$content == y_classify[[1]]$content)
+  expect_false(x_extract == y_extract)
+  expect_false(x_classify == y_classify)
 })
diff --git a/r/tests/testthat/test-m-backend-submit.R b/r/tests/testthat/test-m-backend-submit.R
index f57d7ba..6b14347 100644
--- a/r/tests/testthat/test-m-backend-submit.R
+++ b/r/tests/testthat/test-m-backend-submit.R
@@ -52,7 +52,7 @@ test_that("ellmer code is covered", {
       x = test_txt,
       prompt = list(list(content = "test"))
     ),
-    as.list(test_txt)
+    test_txt
   )
   expect_snapshot(
     m_backend_submit(