diff --git a/docs/evaluation/capturing-feedback.mdx b/docs/evaluation/capturing-feedback.mdx
index aa434f51d6e7b..2b4c24eb5c6fc 100644
--- a/docs/evaluation/capturing-feedback.mdx
+++ b/docs/evaluation/capturing-feedback.mdx
@@ -11,6 +11,8 @@ import {
 
 # Working with feedback
 
+This guide will walk you through feedback in LangSmith. For more end-to-end examples incorporating feedback into a workflow, see the [LangSmith Cookbook](https://github.com/langchain-ai/langsmith-cookbook/tree/main/feedback-examples).
+
 Feedback comes in two forms: automated and human. Both are key to delivering a consistently high quality application experience.
 
 Automated feedback is generated any time you use the `run_on_dataset` method to test your component on a dataset or any time you call `client.evaluate_run(run_id, run_evaluator)`. This guide will focus on human feedback using a common workflow.
diff --git a/docs/tracing/tracing-faq.mdx b/docs/tracing/tracing-faq.mdx
index 7a7b719c36d6b..f496614cb8dfe 100644
--- a/docs/tracing/tracing-faq.mdx
+++ b/docs/tracing/tracing-faq.mdx
@@ -84,7 +84,7 @@ from langchain.prompts import PromptTemplate\n
 llm = ChatOpenAI(temperature=0, tags=["my-llm-tag"])
 prompt = PromptTemplate.from_template("Say {input}")
 chain = LLMChain(llm=llm, prompt=prompt, tags=["my-bash-tag", "another-tag"])\n
-chain("Hello, World!", tags=["shared-tags"])
+chain.invoke("Hello, World!", {"tags": ["shared-tags"]})
 `),
     TypeScriptBlock(`import { LLMChain } from "langchain/chains";
 import { PromptTemplate } from "langchain/prompts";
@@ -112,7 +112,7 @@ Similar to tags, LangSmith permits associating arbitrary key-value pairs as meta
 from langchain.chains import LLMChain\n
 chat_model = ChatOpenAI()
 chain = LLMChain.from_string(llm=chat_model, template="What's the answer to {input}?")\n
-chain({"input": "What is the meaning of life?"}, metadata={"my_key": "My Value"})`),
+chain.invoke({"input": "What is the meaning of life?"}, {"metadata": {"my_key": "My Value"}})`),
     TypeScriptBlock(`import { ChatOpenAI } from "langchain/chat_models/openai";
 import { LLMChain } from "langchain/chains";
 import { PromptTemplate } from "langchain/prompts";\n
@@ -147,7 +147,7 @@ Inject the experiment ID or testing variant(s) in as metadata values, then use t
 from langchain.chains import LLMChain\n
 chat_model = ChatOpenAI()
 chain = LLMChain.from_string(llm=chat_model, template="What's the answer to {input}?")\n
-chain({"input": "What is the meaning of life?"}, metadata={"variant": "abc123"})`),
+chain.invoke({"input": "What is the meaning of life?"}, {"metadata": {"variant": "abc123"}})`),
     TypeScriptBlock(`import { ChatOpenAI } from "langchain/chat_models/openai";
 import { LLMChain } from "langchain/chains";
 import { PromptTemplate } from "langchain/prompts";\n
diff --git a/tests/py_unit_tests/tracing/test_tracing-faq.py b/tests/py_unit_tests/tracing/test_tracing-faq.py
index aaab4482fe448..48ada365b6e2b 100644
--- a/tests/py_unit_tests/tracing/test_tracing-faq.py
+++ b/tests/py_unit_tests/tracing/test_tracing-faq.py
@@ -14,6 +14,7 @@ async def test_code_block_0():
 @pytest.mark.asyncio
 async def test_code_block_1():
     from langchain.callbacks.tracers import LangChainTracer
+    
     tracer = LangChainTracer(project_name="My Project")
     chain.invoke({"query": "How many people live in canada as of 2023?"}, config={"callbacks": [tracer]})
     
@@ -28,7 +29,7 @@ async def test_code_block_2():
     prompt = PromptTemplate.from_template("Say {input}")
     chain = LLMChain(llm=llm, prompt=prompt, tags=["my-bash-tag", "another-tag"])
     
-    chain("Hello, World!", tags=["shared-tags"])
+    chain.invoke("Hello, World!", {"tags": ["shared-tags"]})
     
 
 @pytest.mark.asyncio
@@ -39,7 +40,7 @@ async def test_code_block_3():
     chat_model = ChatOpenAI()
     chain = LLMChain.from_string(llm=chat_model, template="What's the answer to {input}?")
     
-    chain({"input": "What is the meaning of life?"}, metadata={"my_key": "My Value"})
+    chain.invoke({"input": "What is the meaning of life?"}, {"metadata": {"my_key": "My Value"}})
 
 @pytest.mark.asyncio
 async def test_code_block_4():
@@ -49,7 +50,7 @@ async def test_code_block_4():
     chat_model = ChatOpenAI()
     chain = LLMChain.from_string(llm=chat_model, template="What's the answer to {input}?")
     
-    chain({"input": "What is the meaning of life?"}, metadata={"variant": "abc123"})
+    chain.invoke({"input": "What is the meaning of life?"}, {"metadata": {"variant": "abc123"}})
 
 @pytest.mark.asyncio
 async def test_code_block_5():